Multimodal biomarkers predictive of transdiagnostic symptom severity

ABSTRACT

The method for evaluating mental health of a patient includes displaying a series of inquiries from mental health questionnaires on a display device. Each inquiry of the series of inquiries includes text and a set of answers. A series of selections is received from a user interface. Each selection of the series of selections is representative of an answer of the set of answers for each corresponding inquiry in the series of inquiries. Unprocessed MRI data are received. The unprocessed MRI data correspond to a set of MRI images of a biological structure associated with a patient. Using a machine learning model, the series of selections and the unprocessed MRI data are processed. The series of selections being processed corresponds to the series of inquiries. A symptom severity indicator for a mental health category of the patient is outputted.

CROSS-REFERENCE TO RELATED APPLICATION

This application is the National Phase of International ApplicationPCT/US2019/048809, filed Aug. 29, 2019, which designated the UnitedStates, which claims priority to and the benefit of U.S. ProvisionalPatent No. 62/726,009 filed Aug. 31, 2018 and U.S. Provisional PatentNo. 62/840,178 filed Apr. 29, 2019, each of which is hereby incorporatedby reference herein in its entirety.

TECHNICAL FIELD

The present disclosure relates to biomarkers, and more specifically, tothe use of machine learning and multi-modal biomarkers to predictsymptom severity.

BACKGROUND

Conventional clinical psychiatric practice focuses on diagnosticclassification, relying on making diagnoses and recommending treatmentfor disorders based solely on clinical phenomenology. This approachhampers prognostic assessment, treatment, and drug development becauseit does not take into account the neurobiology of patients.

Biomarkers are biological characteristics that can serve as indicatorsfor normal or pathological processes or responses to intervention.Biomarker development within psychiatry lags behind other areas ofmedicine, partly because psychiatric syndromes have a far more complexrelationship between the biology and severity of the symptoms than otherfields of medicine. Conventional clinical practice does not providebiological measures which are able to robustly describe complexpsychiatric syndromes. Additionally, conventional diagnostic biomarkerapproaches do not fully account for the heterogeneity of symptoms underthe umbrella of a single diagnosis or the shared symptoms betweenmultiple diagnoses. Clinical symptoms, such as depressed/elevated mood,anhedonia, and anxiety, often span multiple diagnostic categories.

Conventional research suggests that derived symptom dimensions areassociated with resting-state functional magnetic resonance imaging(rs-fMRI) connectivity transdiagnostically (i.e., where multiplediagnostically-distinct patient groups are modeled together). Otherresearch found links between task-based fMRI activation or rs-fMRIconnectivity and existing anhedonic, depressive, and anxiety symptomdimensions transdiagnostically. This symptom-to-neurophysiological-linksin conventional research, however, lacks a predictive framework, and anyinsights from neuroimaging-based biomarker research have not translatedinto clinical practice. Therefore, conventional clinical practice doesnot provide transdiagnostic, multimodal predictive models of symptomseverity which include neurobiological characteristics.

In particular, conventional research does not identify whether symptomshave a more circumscribed biological basis to few brain networks asproposed in a recent taxonomy or to multiple networks. Additionally, itis not known whether a single, broad self-report clinical assessment(like the Temperament and Character Inventory or the Hopkins SymptomChecklist, as known in the art) or multiple, more specific instrumentsare better at assessing multiple symptoms.

SUMMARY

Aspects of the present disclosure include a system for evaluating mentalhealth of a patient. The system comprises a display device, a userinterface, a memory, and a control system. The memory contains machinereadable medium, comprising machine executable code. The machineexecutable code stores instructions for performing a method. The controlsystem is coupled to the memory, and includes one or more processors.The control system is configured to execute the machine executable codeto cause the control system to perform the method. The method includesdisplaying a series of inquiries from mental health questionnaires onthe display device. Each inquiry of the series of inquiries includestext and a set of answers. A series of selections is received from theuser interface. Each selection of the series of selections isrepresentative of an answer of the set of answers for each correspondinginquiry in the series of inquiries. Unprocessed MRI data are received.The unprocessed MRI data correspond to a set of MM images of abiological structure associated with the patient. Using a machinelearning model, the series of selections and the unprocessed MM data areprocessed. The series of selections being processed corresponds to theseries of inquiries. A symptom severity indicator for a mental healthcategory of the patient is outputted.

In some aspects, the unprocessed MM data corresponds to MRI data for abrain of the patient. In some aspects, the unprocessed MRI data includesfMRI data. In some aspects, the control system is further configured topreprocess the unprocessed MRI data to identify a plurality of features.

In some aspects, the mental health category of the patient comprises atleast one of: depression, anxiety, and anhedonia. In some aspects, thesymptom severity indicator for the mental health category isquantitative.

In some aspects, the machine learning model is at least one of: ageneralized linear model, a regression model, a supervised regressionmethod, a logistical regression model, random forest, lasso, and anelastic net.

In some aspects, the machine learning model is generated by receivinglabeled training data for a plurality of individuals. The labeledtraining data is indicative of whether each of the plurality ofindividuals has one or more mental health disorders and a severity ofsymptoms corresponding to the one or more mental health disorders. Thelabeled training data includes unprocessed MRI data recorded for each ofthe plurality of individuals, and a series of selections correspondingto the series of inquiries for each of the plurality of individuals. Themachine learning model is further generated by determining a pluralityof features based on the received labeled training data. Based on thedetermined plurality of features, an initial machine learning model istrained in a supervised manner. Based on the training of the initialmachine learning model, importance measures for each of the plurality offeatures are extracted. Based on the extracted importance measures forthe plurality of features, a plurality of subset machine learning modelsis generated. A classification performance of the generated plurality ofsubset machine learning models is evaluated. At least one of the subsetmachine learning models is selected as the machine learning model.

In some aspects, the machine learning model is trained on clinicalscales data corresponding to the plurality of individuals. In someaspects, the machine learning model is trained on fMRI full connectivitydata corresponding to the plurality of individuals.

In some aspects, the machine learning model is trained on sMRI datacorresponding to the plurality of individuals. The sMRI data includecortical volume data, cortical thickness data, and cortical surface areadata.

In some aspects, the machine learning model is trained on input datacorresponding to the plurality of individuals. In an exemplary aspect,for each individual, the input data include clinical scales data andfMRI data. In another exemplary aspect, for each individual, the inputdata include clinical scales data and sMRI data. In yet anotherexemplary aspect, for each individual, the input data include fMRI dataand sMRI data. In a further exemplary aspect, for each individual, theinput data include fMRI data, clinical scales data, and sMRI data.

Additional aspects of the present disclosure include a system forevaluating mental health of a patient. The system includes a displaydevice, a user interface, a memory, and a control system. The memoryincludes machine readable medium comprising machine executable code. Themachine executable code stores instructions for performing a method. Thecontrol system is coupled to the memory, and includes one or moreprocessors. The control system is configured to execute the machineexecutable code to cause the control system to receive a selection ofanswers from the user interface. The selection of answers corresponds toeach question in a series of questions from mental healthquestionnaires. Unprocessed MM data are received. The unprocessed MRIdata correspond to a set of MRI images of a biological structure. Usinga machine learning model, the selection of answers and the unprocessedMRI data are processed. A symptom severity indicator for a mental healthcategory of the patient is outputted.

Further aspects of the present disclosure include a machine learningtraining system. The machine learning training system includes at leastone nontransitory processor-readable storage medium, and at least oneprocessor communicatively coupled to the at least one nontransitoryprocessor-readable storage medium. The at least one nontransitoryprocessor-readable storage medium stores at least one ofprocessor-executable instructions or data. In operation, the at leastone processor configured to receive labeled training data for aplurality of individuals. The labeled training data are indicative ofwhether each of the plurality of individuals has one or more mentalhealth disorders and a severity of symptoms corresponding to the one ormore mental health disorders. The labeled training data include MRI datarecorded for each of the plurality of individuals, and a selection ofanswers to the series of questions for each of the plurality ofindividuals. A plurality of features is determined from the labeledtraining data. Based on the plurality of features, an initial machinelearning model is trained in a supervised manner. Based on the trainingof the initial machine learning model, importance measures for each ofthe plurality of features are extracted. Based on the extractedimportance measures for the plurality of features, a plurality of subsetmachine learning models is generated. A classification performance ofthe generated plurality of subset machine learning models is evaluated.At least one of the subset machine learning models is selected as themachine learning model. The plurality of features of the machinelearning model is stored in the at least one nontransitoryprocessor-readable storage medium for subsequent use as a screeningtool.

In some aspects, the machine learning system further includes using thefeatures of the machine learning model as a screening tool to assess atleast one of intermediate or end-point outcomes in at least one clinicaltrial testing for treatment responses.

In some aspects, each feature in the plurality of features comprises animportance measure. Each of the subset machine learning models includesa sequentially lower number of features than a following subset machinelearning model. The features are selected for each subset machinelearning model based on a highest importance measure.

In some aspects, the selected subset machine learning model includes aportion of the plurality of features. The portion selected from featuresincludes an importance measure above a threshold value. In some aspects,each of the subset machine learning models includes a differentselection of the portion of the plurality of features. In some aspects,each of the subset machine learning models includes a differentcombination of the plurality of features. In some aspects, at leasttwenty features of the plurality of features have an importance measureabove the threshold value. The portion includes at least ten featuresand less than twenty features.

In some aspects, the machine learning model is configured to output asymptom severity indicator identifying a severity of at least one mentalhealth symptom of a patient.

In some aspects, training the initial machine learning model includesusing k-fold cross validation with logistic regression.

In some aspects, the labeled training data further comprises at leastone of functional measurement data or physiological measurement data.

The above summary is not intended to represent each embodiment or everyaspect of the present disclosure. Rather, the foregoing summary merelyprovides an example of some of the novel aspects and features set forthherein. The above features and advantages, and other features andadvantages of the present disclosure, will be readily apparent from thefollowing detailed description of representative embodiments and modesfor carrying out the present disclosure, when taken in connection withthe accompanying drawings and the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other advantages of the present disclosure will becomeapparent upon reading the following detailed description and uponreference to the drawings.

FIG. 1A illustrates X-Y plots of number of features versus predictedoutcome scores, according to some implementations of the presentdisclosure;

FIG. 1B illustrates a comparison of measured outcome scores andpredicted outcome scores, according to some implementations of thepresent disclosure;

FIGS. 2A-2B illustrate comparisons of measured values and predictedvalues under dysregulated mood models, according to some implementationsof the present disclosure;

FIGS. 2C-2D illustrate comparisons of measured values and predictedvalues under anhedonia models, according to some implementations of thepresent disclosure;

FIG. 2E-2F illustrate comparisons of measured values and predictedvalues under anxiety models, according to some implementations of thepresent disclosure;

FIG. 3A illustrates exemplary proportions by feature types in some ofthe disclosed models, according to some implementations of the presentdisclosure;

FIG. 3B illustrates exemplary proportions by the top 25% of the featuretypes in some of the disclosed models, according to some implementationsof the present disclosure;

FIGS. 4A-4B illustrate pie charts of exemplary proportions of featuresfrom each scale under dysregulated mood models, according to someimplementations of the present disclosures

FIGS. 4C-4D illustrate pie charts of exemplary proportions of featuresfrom each scale under anhedonia models, according to someimplementations of the present disclosure;

FIGS. 4E-4F illustrate pie charts of exemplary proportions of featuresfrom each scale under anxiety models, according to some implementationsof the present disclosure;

FIGS. 5A-5B illustrate connectivity matrices and region of interest(ROI) locations for fMRI connectivity features in some of the disclosedmodels predicting mood outcome variables, according to someimplementations of the present disclosure;

FIGS. 5C-5D illustrate connectivity matrices and ROI locations for fMRIconnectivity features in some of the disclosed models predictinganhedonia outcome variables, according to some implementations of thepresent disclosure;

FIGS. 5E-5F illustrate connectivity matrices and ROI locations for fMRIconnectivity features in some of the disclosed models predicting anxietyoutcome variables, according to some implementations of the presentdisclosure;

FIGS. 6A-6I illustrate distributions of in-scanner motion measurements,having scales data, sMRI data, and fMRI data as input, according to someimplementations of the present disclosure;

FIGS. 7A-7I illustrate comparisons of in-scanner motion measurements,having scales data, sMRI data, and fMRI data as input, and grouped bydiagnoses, according to some implementations of the present disclosure;

FIGS. 8A-8C illustrate distributions of outcome measures, having scalesdata, sMRI data, and fMRI data as input, according to someimplementations of the present disclosure;

FIG. 9 illustrates a feature stability for the Elastic Net, havingscales data, sMRI data, and fMRI data as input, according to someimplementations of the present disclosure;

FIG. 10A illustrates binary heat maps for fMRI connectivity featuresunder dysregulated mood models, according to some implementations of thepresent disclosure;

FIG. 10B illustrates binary heat maps for fMRI connectivity featuresunder anhedonia models, according to some implementations of the presentdisclosure;

FIG. 10C illustrates binary heat maps for fMRI connectivity featuresunder anxiety models, according to some implementations of the presentdisclosure;

FIGS. 11A-11B illustrate results of permutation tests under dysregulatedmood models, according to some implementations of the presentdisclosure;

FIGS. 11C-11D illustrate results of permutation tests under anhedoniamodels, according to some implementations of the present disclosure;

FIGS. 11E-11F illustrate results of permutation tests under anxietymodels, according to some implementations of the present disclosure;

FIG. 12A illustrates proportions of features from each scale havingclinical scales data only as input under dysregulated mood models,according to some implementations of the present disclosure;

FIG. 12B illustrates proportions of features from each scale havingclinical scales data only as input under anhedonia models, according tosome implementations of the present disclosure;

FIG. 12C illustrates proportions of features from each scale havingclinical scales data only as input under anxiety models, according tosome implementations of the present disclosure;

FIG. 13 illustrates current medication usage status, grouped bymedication class, for groups of participants, according to someimplementations of the present disclosure;

FIG. 14 illustrates an exemplary system for implementing variousmethodologies disclosed herein, according to some implementations of thepresent disclosure;

FIG. 15 illustrates an exemplary methodology for determining a symptomseverity indicator for a patient, according to some implementations ofthe present disclosure;

FIG. 16 illustrates an exemplary methodology for using a machinelearning model to analyze input and output a symptom severity indicator,according to some implementations of the present disclosure;

FIGS. 17A-17B illustrate a block diagram of an MRI system used toacquire NMR data, according to some implementations of the presentdisclosure; and

FIG. 18 illustrates a block diagram of a transceiver which forms part ofthe MRI system of FIG. 17A.

While the present disclosure is susceptible to various modifications andalternative forms, specific embodiments have been shown by way ofexample in the drawings and will be described in detail herein. Itshould be understood, however, that the present disclosure is notintended to be limited to the particular forms disclosed. Rather, thepresent disclosure is to cover all modifications, equivalents, andalternatives falling within the spirit and scope of the presentdisclosure as defined by the appended claims.

DETAILED DESCRIPTION

The present disclosure is described with reference to the attachedfigures, where like reference numerals are used throughout the figuresto designate similar or equivalent elements. The figures are not drawnto scale, and are provided merely to illustrate the instant disclosure.Several aspects of the disclosure are described below with reference toexample applications for illustration. It should be understood thatnumerous specific details, relationships, and methods are set forth toprovide a full understanding of the disclosure. One having ordinaryskill in the relevant art, however, will readily recognize that thedisclosure can be practiced without one or more of the specific details,or with other methods. In other instances, well-known structures oroperations are not shown in detail to avoid obscuring the disclosure.The present disclosure is not limited by the illustrated ordering ofacts or events, as some acts may occur in different orders and/orconcurrently with other acts or events. Furthermore, not all illustratedacts or events are required to implement a methodology in accordancewith the present disclosure.

Aspects of the present disclosure can be implemented using one or moresuitable processing device, such as general-purpose computer systems,microprocessors, digital signal processors, micro-controllers,application-specific integrated circuits (ASIC), programmable logicdevices (PLD), field-programmable logic devices (FPLD),field-programmable gate arrays (FPGA), mobile devices such as a mobiletelephone or personal digital assistants (PDA), a local server, a remoteserver, wearable computers, tablet computers, or the like.

Memory storage devices of the one or more processing devices can includea machine-readable medium on which is stored one or more sets ofinstructions (e.g., software) embodying any one or more of themethodologies or functions described herein. The instructions canfurther be transmitted or received over a network via a networktransmitter receiver. While the machine-readable medium can be a singlemedium, the term “machine-readable medium” should be taken to include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore sets of instructions. The term “machine-readable medium” can alsobe taken to include any medium that is capable of storing, encoding, orcarrying a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies of thevarious embodiments, or that is capable of storing, encoding, orcarrying data structures utilized by or associated with such a set ofinstructions. The term “machine-readable medium” can accordingly betaken to include, but not be limited to, solid-state memories, opticalmedia, and magnetic media. A variety of different types of memorystorage devices, such as a random access memory (RAM) or a read-onlymemory (ROM) in the system or a floppy disk, hard disk, CD ROM, DVD ROM,flash, or other computer-readable medium that is read from and/orwritten to by a magnetic, optical, or other reading and/or writingsystem that is coupled to the processing device, can be used for thememory or memories.

Overview

The present disclosure provides predictive models for three commonsymptoms in psychiatric disorders (dysregulated mood, anhedonia, andanxiety). In some examples, the predictive models successfully utilizedata from the Consortium for Neuropsychiatric Phenomics (CNP) dataset,which includes clinical scale assessments, resting-state functional MRI(rs-fMRI), and structural-MRI (sMRI) imaging measures. The data isprovided from healthy control participants and patients withschizophrenia, bipolar disorder, and attention deficit and hyperactivitydisorder (ADHD). In addition, examining symptoms in transdiagnosticgroups can be highly informative. The CNP dataset further includes threepatient groups with shared genetic risk and includes MRI and clinicalscale data for each patient.

According to some implementations of the present disclosure, thedisclosed predictive models, using the disclosed feature selectionapproach discussed herein, were able to explain 65-90% of varianceacross the three symptom domains. In some examples, the predictivemodels explain 22% of variance without using the feature selectionapproach. For feature selection, the present disclosure provides adata-driven feature selection approach from the field of machinelearning, relying on importance-weighted forward selection, to searchthrough a high-dimensional space and optimize model performance andinterpretability. This importance-ranked, forward selection modelingapproach searches for the most predictive input features from a set ofclinical scale measures, sMRI measures, and rs-fMRI measures. Notably,this data-driven way of selecting feature subsets led to multimodalneurobehavioral models with consistently high predictability acrossmultiple symptom domains and to high interpretability enabled byimportance scores for individual features. Thus, the present disclosuredemonstrates that a shorter, broadly-applicable five-minute rs-fMRI scanand a small set of clinical scale assessments can be used to predict apanel of core symptoms commonly found in various psychiatric disorders.

Exemplary methods involve combining rs-fMRI with select questions fromclinical scales; this enables high levels of prediction of symptomseverity across diagnostically distinct patient groups. In someexamples, connectivity measures beyond a few intrinsic RSNs may carryrelevant information for symptom severity.

Overall, Elastic Net regression models with all three input featuretypes explained the most variance. However, features from the differentmodalities were not equally represented in the models when evaluatingfeature importance. The individual, edge-level fMRI connectivitymeasures between specific network nodes dominated across symptom models,with self-report clinical scales being also highly predictive. Thesemodels maximize predictability (in terms of variance) for models whoseregression coefficients can be used for interpretation, includingfeatures that can be assessed for various clinical and scientificinsights.

The transdiagnostic symptom-based approach of the present disclosureprovides more options for predicting longitudinal and treatment outcomesbeyond those afforded by conventional clinical diagnosis alone. Thedisclosed approach allows clinicians to estimate symptom severity (andcan be used as practical tools in other clinical applications) inbroader populations, where the patient might not have an initialdiagnosis. Also, using a biological marker of a symptom to track andpredict eventual treatment response can introduce clinical efficienciesduring a medical treatment. If the biomarker detects treatment-relatedchanges sooner than behavioral/symptomatic changes, it can indicate if apatient is responding to an intervention earlier than conventionaltreatments; this can be the basis for an earlier continue/switch/endtreatment decision by a clinician. In some examples, predicting symptomvariation itself and symptom severity could predict treatment responsealtogether.

Therefore, the present disclosure seeks to integrate functionalneuroimaging with other data modalities. Combining biological andclinical variables has led to improved predictability in cancer modelsbut is yet underexplored in psychiatry, outside of the presentdisclosure. The predictive framework is especially powerful beyondassociative frameworks (such as correlation analyses), as it not onlyallows multivariate modeling to deal with high-dimensional, multimodaldata but also provides testing of predictive value and generalizabilityof those models on an independent sample.

The disclosed models retain a high level of interpretability, enablingseveral clinical and scientific insights, including: (1) structuralfeatures do not substantially contribute to the predictive strength ofthe models, (2) the Temperament and Character Inventory scale is avaluable predictor of symptom variation across diagnoses, and (3)predictive rs-fMRI connectivity features are widely distributed acrossmany intrinsic resting-state networks (RSN). This disclosed models alsoclarify the biological basis of symptoms or the utility of differentclinical scales for prediction.

Exemplary Systems and Methodologies

The present disclosure contemplates that a variety of systems can beused to perform various embodiments of the present disclosure. Referringnow to FIG. 14, an exemplary system 1400 is shown, which can beconfigured to perform various methods of the present disclosure,including methods 1500 and 1600 of FIGS. 15 and 16, respectively. Inparticular, system 1400 includes a display 1402, a user interface 1404,a control system 1406, and a memory 1408. In some examples, the system1400 further includes one or more servers 1410.

The user interface 1404 is configured to receive input from a user. Forexample, the user interface 1404 can be a keyboard, a touchscreen, amobile device, or any other device for receiving input, as known in theart. The user enters data on the user interface 1404 in response toprompts on the display 1402. For example, the display 1402 outputs aseries of mental health questions, and the user inputs an answer to eachquestion on the user interface 1404. In some examples, the userinterface 1404 directly displays the input on display 1402 and relaysthe data to the control system 1406. In some examples, the data is thenstored in the memory 1408.

The display 1402 is configured to receive data from the control system1406 and the user interface 1404. For example, the display 1402 displaysinput received from the user interface 1404; in some examples, the datais first sent to the control system 1406, which then processes the dataand instructs the display 1402 according to the processed data. In otherexamples, the display 1402 displays data received from the controlsystem 1406. Exemplary data from the control system 1406 includesquestions from a mental health questionnaire, answer boxes, answeroptions, answer data, or a symptom severity indicator related data. Insome examples, the display 1402 is on a smart phone.

The present disclosure also contemplates that more than one display 1402can be used in system 1400, as would be readily contemplated by a personskilled in the art. For example, one display can be viewable by apatient, while additional displays are visible to researchers and not tothe patient. The multiple displays can output identical or differentinformation, according to instructions by the control system 1406.

The control system 1406 can be communicatively coupled to the display1402, the user interface 1404, and the memory 1408. Further, the controlsystem 1406 can be communicatively coupled to the server 1410. Forexample, the communication can be wired or wireless. The control system1406 is configured to perform any methods as contemplated according toFIGS. 15-16 (discussed further below). The control system 1406 canprocess and/or store input from the display 1402, the user interface1404, and the memory 1408. In some examples, the methodologies disclosedherein can be implemented, via the control system 1406, on the server1410. It is also contemplated that the server 1410 includes a pluralityof servers, and can be remote or local. Optionally, the control systemand/or the memory 1408 may be incorporated into the server 1410.

In some examples, system 1400 can be a unitary device, for example, asmart phone, which includes a display 1402, a user interface 1404, acontrol system 1406, and a memory 1408.

Turning now to FIG. 15, an exemplary methodology 1500 is discussed fordetermining a symptom severity indicator for a patient. Additionaldetails and alternate steps for methodology 1500 are discussed furtherwith regards to FIGS. 1A-13 and the corresponding description.

Methodology 1500 begins at step 1510 which provides for displaying aseries of questions. An exemplary series of questions includes questionsfrom mental health questionnaires, and includes both text and answersfor each question.

Methodology 1500 then provides for, at step 1520, receiving answers foreach of the series of questions (the questions provided for in step1510). In some examples, the answers are received at a user interface(e.g., user interface 1404 of FIG. 14). In some examples, the answersinclude selection of a multiple choice question, a textual response, orany other user input as contemplated by one skilled in the art. In someexamples, the answers are retrieved from a record entry corresponding toone patient in a database of patient records. This database can bestored in memory 1408 of FIG. 14, for example. In some examples, thedatabase can be stored in the sever 1410 of FIG. 14. In some examples,methodology 1500 begins directly at step 1520.

Step 1530 provides for receiving unprocessed MRI data. The unprocessedMRI data corresponds to a set of MRI images of a biological structure.In some examples, the MM data corresponds to MM data for a patient'sbrain (i.e., the same patient who provided answers at step 1520). TheMRI data can include task-based fMRI data, rs-fMRI data, and/or sMRIdata. In additional examples of step 1530, methodology 1500 can providefor receiving clinical scales data. In some examples of step 1530,methodology 1500 provides for receiving processed MRI data.

Step 1540 then provides for processing, using a machine learning model,the selection of answers from step 1520 and the data received at step1530. In some examples of methodology 1500, the data received at step1530 is preprocessed to identify a plurality of features.

At step 1550, methodology 1500 provides for outputting a symptomseverity indicator for a mental health category of a user. In someexamples of the present disclosure, step 1550 performs processing of theanswers and the received data as discussed further below with respect tomethod 1600 of FIG. 16. The mental health category can include any of(1) depression, (2) anxiety, and (3) anhedonia. In some aspects, thesymptom severity indicator for the mental health category isquantitative. For example, the symptom severity indicator includes anumerical scale (such as 1 to 5, 1 to 10, etc.), a color scale (green toyellow to red), an emoji scale, or the like, or in any combinationthereof.

In some examples, the symptom severity indicators are scores across ascale. For example, the score can range from zero to forty (0-40); zeroindicates no evidence of a symptom, and forty indicates that the patientis severely symptomatic. In some examples, each questionnaire to measuresymptom severity can have a different scale. Any other symptom severityscale can be used as well, as would be readily apparent to one skilledin the art.

Referring now to methodology 1600 of FIG. 16, an exemplary methodologyis shown for using a machine learning model to analyze input and outputa symptom severity indicator, according to various embodiments of thepresent disclosure. In some examples, the machine learning model is anyof: a generalized linear model, a regression model, a supervisedregression method, random forest model, LASSO model, and an elastic netmodel. In some examples, the machine learning model is any of the modelsand algorithms discussed further below. In one embodiment of method1600, the present disclosure provides two regularized general linearmodel regression algorithms, LASSO and Elastic Net, and one non-linearregression model algorithm, Random Forest. Elastic Net in particular canbe used when the number of predictor variables is much greater than thenumber of samples.

In step 1610, methodology 1600 provides for receiving labeled trainingdata regarding mental health disorder status for a plurality ofindividuals. In some examples, the labeled training data identifieswhether each of the individuals has one or more mental health disordersand the severity of their symptoms. The labeled training data includes,for each individual, a selection of answers to mental healthquestionnaires and includes MM data. The MRI data can be task-based fMRIdata, sMRI data, and/or rs-fMRI data. In some examples, the labeledtraining data includes, for each individual, an indication of any of:whether the individual is healthy, whether the individual has a generalmental health issue, whether the individual has one or more specificmental health disorders, whether the individual is at risk of developinga general mental health issue, or whether the individual is at risk ofdeveloping one or more specific mental health disorders. In someexamples, the labeled training data includes another functional and/orphysiological measurement dataset, as known in the art.

In step 1620, methodology 1600 provides for determining features fromthe labeled training data of step 1610. The features are determinedaccording to any methods, as known in the art. In other examples,features will not be determined from the labeled training data and theywill be input directly into the algorithm.

In step 1630, methodology 1600 provides for training an initial machinelearning model in a supervised manner, based on the features determinedin step 1620. In some examples, training this initial machine learningmodel includes using k-fold cross-validation with LASSO and Elastic Netregression.

In some examples, training this initial machine learning model in step1630 includes training the model on clinical scales data correspondingto the plurality of individuals.

In some examples, training this initial machine learning model in step1630 includes training the model on fMRI full connectivity datacorresponding to the plurality of individuals.

In some examples, training this initial machine learning model in step1630 includes training the model on sMRI data corresponding to aplurality of individuals, the sMRI data including cortical volume data,cortical thickness data, and cortical surface area data.

In some examples, training this initial machine learning model in step1630 includes training the model on input data corresponding to theplurality of individuals, wherein, for each individual, the input dataincludes clinical scales data and fMRI data.

In some examples, training this initial machine learning model in step1630 includes training the model on input data corresponding to theplurality of individuals, wherein, for each individual, the input dataincludes clinical scales data and sMRI data.

In some examples, training this initial machine learning model in step1630 includes training the model on input data corresponding to theplurality of individuals, wherein, for each individual, the input datacomprises fMRI data and sMRI data.

In some examples, training this initial machine learning model in step1630 includes training the model on input data corresponding to theplurality of individuals, wherein, for each individual, the input datacomprises fMRI data, clinical scales data, and sMRI data. Thisparticular combination of input data provides a high r² metric(calculated on an untouched evaluation set data to avoid biasing andoverfitting our models) when using Elastic Net across the differentoutcome variables.

In step 1640, methodology 1600 provides for extracting importancemeasures for each of the features. These importance measures areselected based on the trained initial machine learning model.

In step 1650, methodology 1600 provides for generating a plurality ofsubset machine learning models, based on the extracted importancemeasures of step 1640. In step 1660, methodology 1600 provides forevaluating a regression performance of the generated subset machinelearning models from step 1650. In some examples, each of the subsetmachine learning models includes a different selection of features. Insome examples, the subset machine learning models include only featureswith an importance measure above a threshold value. In some examples,the features are ranked based on the importance measure. In someexamples, each of the subset machine learning models includes asequentially lower number of features than a following subset machinelearning model, wherein the features are selected for each subsetmachine learning model based on a highest importance measure.

In step 1670, methodology 1600 provides for selecting one of the subsetmachine learning models as a generalized linear learning model. Theselection is based on the classification performances as evaluated instep 1660. The selected subset machine learning model includes a portionof the plurality of features determined from step 1620. The portion offeatures is selected from features with an importance measure (asdetermined in step 744) above a threshold value. In some examples, morethan one subset machine learning model is selected.

In some examples of step 1670, the threshold value is set so that atleast twenty features of the plurality of features determined in step1620 have an importance measure above the threshold value. In someexamples, the threshold value is set to select a portion of between tenand twenty features.

In some examples of step 1670, the features of the machine learningmodel are stored in a non-transitory processor-readable storage medium(e.g., memory 1408 of FIG. 14). The features can then be later used as ascreening tool. In some examples, the screening tool can output asymptom severity indicator of a mental health condition. In someexamples, the screening tool assesses intermediate and/or end-pointoutcomes in clinical trial testing for treatment responses.

Therefore, the selected machine learning model can then be used toprocess any of the input data as provided for in the present disclosure.

In other examples of steps 1660 and 1670, one hundred twenty-six (126)sets of models can be built to examine all permutations of seven featureset inputs, three modeling algorithms, and six outcome variables. Theseven feature set inputs include: (1) clinical scales data; (2) fMRIfull connectivity data; (2) sMRI cortical volume, cortical area, andcortical thickness data; (3) clinical scales data and fMRI data; (4)clinical scales data and sMRI data; (5) sMRI data and fMRI data; and (7)clinical scales data, fMRI data, and sMRI data. The three modelingalgorithms include anxiety, depression, and anhedonia. The six outcomevariables can be symptom severity scores, as discussed herein.

As discussed herein, conventional diagnostic biomarker approaches do notfully account for the heterogeneity of symptoms under the umbrella of asingle diagnosis or the shared symptoms between multiple diagnoses. Itmust be noted that conventional clinical practice does not providetransdiagnostic, multimodal predictive models of symptom severity. Thus,based on the seven feature set input, such as the examples disclosedherein with regard to steps 1660 and 1670, various combinations offeature types are evaluated as inputs. For example, instead of onlyanalyzing one type of biomarkers, the various combinations of input datainclude single and multimodal feature sets. The experimental data hereinprovides that the multimodal models perform better than those of singlefeature sets. Therefore, the models disclosed herein can be highlypredictive based at least in part on their transdiagnostic and/ormultimodal data input.

Example Application of the Disclosed Models

FIGS. 1A-1B show evaluations using the disclosed models (the models arediscussed further with regard to FIGS. 14-16 and correspondingdescription) using candidate subsets according to a prespecifiedcriterion to find an optimal model. FIG. 1A illustrates X-Y plots ofnumber of features versus predicted outcome scores. FIG. 1A showsexemplary data predicting a total Mood_Bipolar score using Elastic Netand clinical scales data, sMRI data, and fMRI data as input. The medianMSE and median r² are shown to vary with each feature subset (standarddeviation bars are also shown). FIG. 1B compares measured outcome scoresagainst predicted outcome scores; the data demonstrates how closely themodel predictions are to actual outcome scores for individuals. Themodel predictions are trained on a portion of a dataset, while themeasured outcome scores are reserved in a held-out sample, from the samedataset. Additional details of the methodology are discussed furtherwith regards to the experimental methodology.

FIGS. 2A-2F compare measured and predicted values for best models formood, anhedonia, and anxiety. For example: FIGS. 2A-2B illustratecomparisons of measured values and predicted values under dysregulatedmood models; FIGS. 2C-2D illustrate comparisons of measured values andpredicted values under anhedonia models; and FIG. 2E-2F illustratecomparisons of measured values and predicted values under anxietymodels. Each dot in the scatter plot, marked by diagnosis (shown as dotswith different hatching), represents a single participant from theheld-out evaluation set. Their measured symptom severity score is alongthe x-axis, and their predicted symptom severity score is along they-axis. The dashed diagonal line represents a perfect one-to-one linearrelationship between measured and predicted values. FIGS. 2A-3C show howclosely the model predictions are to actual outcome scores forindividuals in the held-out samples for this set of models. In thisexample, there is no particular diagnostic group that is further fromthe measured/predicted line across all six models; this suggests thatthe models generalize across the multiple diagnoses. FIGS. 2A-2C alsodemonstrate that healthy control subjects are generally in the lowerhalf of the symptom score ranges.

FIGS. 3A-3B show exemplary proportions of feature types in some of thedisclosed models. FIG. 3A shows a proportion of all features returned bythe model. The densest hatching represents the proportion of featuresfrom scales, the medium density hatching represents proportion from fMRIconnectivity measures, and the least dense hatching representsproportion from sMRI measures. FIG. 3B shows a proportion of featuretypes in the top 25% of features returned by the model; this indicatesthat most of the disclosed models have equal or greater proportion ofscale features than among all the non-zero features.

FIGS. 4A-4F show exemplary data for a proportion of features from eachscale for the best model predicting mood, anhedonia, and anxiety. Forexample, FIGS. 4A-4B illustrate pie charts of exemplary proportions offeatures from each scale under dysregulated mood models; FIGS. 4C-4Dillustrate pie charts of exemplary proportions of features from eachscale under anhedonia models; and FIGS. 4E-4F illustrate pie charts ofexemplary proportions of features from each scale under anxiety models.Of the features returned by the best model that were scale items, eachpie chart shows the proportion of those items that were from thecorresponding scales for the model for each outcome variable. Forexample, for the Mood/Dep_Hopkins model, 31% of the scale items werefrom the TCI scale, 6% from the Chaphyp scale, etc. This representationof features does not show the sign of the regression coefficient andwhether predictive features indicate increasing or decreasing symptomseverity.

FIGS. 5A-5F show connectivity matrices and ROI locations for fMRIconnectivity features of best models predicting mood (FIGS. 5A-5B),anhedonia (FIGS. 5C-5D), and anxiety (FIGS. 5E-5F) outcome variables.For all non-zero fMRI connectivity features returned by the respectivemodel, the number of individual edges between two nodes is plotted inthe connectivity matrix (shown in the left plots of each of FIGS. 5A-5F)for that model. Each row and column represent a single resting-statenetwork (RSN) from the Power atlas. Darker squares represent morefeatures within or between the given networks with actual feature numbersuperimposed numerically on each square.

Connectivity matrices have the same RSNs listed on both axes, so upperand lower triangles show redundant information. Cortical surface plots(shown in the right plots of each of FIGS. 5A-5F) show the ROI locationsmarked by RSN membership for each model to display the breadth ofnetworks with informative features for each model. Because only corticalsurfaces are shown, no cerebellar nodes were plotted in the brain plots.Network labels are AUD: Auditory, CER: Cerebellar, COTC:Cingulo-opercular Task Control, DM: Default Mode, DA: Dorsal Attention,FPTC: Fronto-parietal Task Control, MEM: Memory Retrieval, SAL:Salience, SSM-H: Sensory/somatomotor Hand, SSM-M: Sensory/somatomotorMouth, SUB: Subcortical, UNC: Uncertain (i.e., miscellaneous regions notassigned to a specific RSN), VA: Ventral Attention, VIS: Visual.

The models with the least complexity are scales-only models shown inFIGS. 6A-6I. FIGS. 6A-6I shows distributions of the scales+sMRI+fMRIcohort's in-scanner motion measurements. FIG. 6A shows percentage offrames that exceeded 0.5 mm. FIG. 6B shows mean framewise displacement(FD). FIG. 6C mean sharp head motion. FIGS. 6D-6I show mean of motionfor each of six motion parameters—x-direction, y-direction, z-direction,pitch, roll, and yaw. Each plot displays the histogram with a Gaussiankernel density estimated distribution superimposed.

FIGS. 7A-7I shows comparisons of in-scanner motion measurements for thescales data, sMRI data, and fMRI data as input, grouped by diagnosis,according to an exemplary methodology of the present disclosure. FIG. 7Ashows box plots of percentage of frames that exceeded 0.5 mm. FIG. 7Bshows mean framewise displacement (FD). FIG. 7C shows mean sharp headmotion. FIGS. 7D-7I show mean of motion for each of six motionparameters—x-direction, y-direction, z-direction, pitch, roll, and yaw.For those group comparisons that yielded significant differences onKruskal Wallis tests (see Supplementary text), post hoc pairwiseWilcoxon rank-sum tests were performed and indicated with stars.Significantly different comparisons are indicated with * for p<0.05, **for p<0.01, and *** for p<0.001.

FIGS. 8A-8C show distributions of outcome measures for scales data, sMRIdata, and fMRI data as input. FIG. 8A shows dysregulated mood models,Hopkins_depression and Bipolar_mood scores. FIG. 8B shows anhedoniamodels, Anhedonia_Chapphy and Anhedonia_Chapsoc scores. FIG. 8C showsanxiety models, Hopkins_anxiety and Bipolar_anxiety scores. Each plotdisplays the histogram with a Gaussian kernel density estimateddistribution superimposed.

Referring now to FIGS. 10A-10C, binary heat maps for fMRI connectivityfeatures of a best model, are shown. For example, FIG. 10A illustratesbinary heat maps for fMRI connectivity features under dysregulated moodmodels; FIG. 10B illustrates binary heat maps for fMRI connectivityfeatures under anhedonia models; and FIG. 10C illustrates binary heatmaps for fMRI connectivity features under anxiety models. For allnon-zero fMRI connectivity features returned by the respective model,the regression coefficients for each individual edge between two nodesis plotted in the connectivity matrix for that model. Each row andcolumn represents a single ROI from the Power atlas, orderedconsistently in both directions. Coefficients have been binarized(positive plotted as stars, negative as dots) for easier viewing ofsparse matrices. Upper and lower triangles show redundant information,so only upper triangles are plotted. Lines delineate intrinsic restingstate networks for easier visualization of network category for eachfeature. Network labels are AUD: Auditory, CER: Cerebellar, COTC:Cingulo-opercular Task Control, DM: Default Mode, DA: Dorsal Attention,FPTC: Fronto-parietal Task Control, MEM: Memory Retrieval, SAL:Salience, SSM-H: Sensory/somatomotor Hand, SSM-M: Sensory/somatomotorMouth, SUB: Subcortical, UNC: Uncertain (i.e., miscellaneous regions notassigned to a specific RSN), VA: Ventral Attention, VIS: Visual.

FIGS. 11A-11F show results of permutation tests of the alternativehypothesis that the best model results were significantly greater thanthe baseline model results (where the outcome variable scores werepermuted across subjects). For example, FIGS. 11A-11B illustrate resultsof permutation tests under dysregulated mood models; FIGS. 11C-11Dillustrate results of permutation tests under anhedonia models; andFIGS. 11E-11F illustrate results of permutation tests under anxietymodels. One hundred (100) permuted models were used to generate theempirical distribution of r² values, and the r² value of the best modelis shown with the star. Distributions extended below negative two (−2)r² in some models, but all models are shown on the same x- and y-scalesfor ease of comparison. All six models had p<0.01.

FIGS. 12A-12C show the proportion of features from each scale for theclinical scales data only as input, according to an exemplarymethodology of the present disclosure. For example, FIG. 12A illustratesproportions of features from each scale having clinical scales data onlyas input under dysregulated mood models; FIG. 12B illustratesproportions of features from each scale having clinical scales data onlyas input under anhedonia models; and FIG. 12C illustrates proportions offeatures from each scale having clinical scales data only as input underanxiety models. The model displayed in FIGS. 12A-12C used Elastic Netwith the median r² value.

FIG. 13 shows current medication usage status, grouped by medicationclass, for groups of participants, according to an exemplary methodologyof the present disclosure. Each bar represents the percent of adiagnostic group that was using a stable medication of that particularclass. Some diagnostic groups did not have any subjects usingmedications from a particular class. No healthy control (HC) subjectswere on psychiatric medications per enrollment criteria.

Contributions of Scale Assessments and fMRI Connectivity Features toModels

In some examples of the present disclosure, rs-fMRI features can begrouped according to intrinsic RSN membership. These networks partiallyoverlap with a proposed taxonomy of symptom-related networks in afocused set of brain regions as known in the prior art. By contrast toconventional research, the systems and methods of the present disclosureidentify that these highly-predictive features are distributed acrosselements of many networks. The wide set of RSNs reflects the relativelywide nature of scale-based symptom constructs in contrast to thetargeting allowed by finely-tuned behavioral tasks. It may also reflecta compilation of different mechanisms across theoretical subgroups ofpatients with differing brain dysregulation. Ultimately, the presentdisclosure provides systems and methods indicating that whole-brainconnectivity between individual nodes is useful when creating models;this whole-brain connectivity is different from relying solely uponsummary metrics of networks such as graph theory metrics, independentcomponents, or more circumscribed ROI approaches to connectivity (theseapproaches are commonly used in the prior art). Specifically, anhedoniamodels of the present disclosure found not only elements of a rewardcircuit but also multiple nodes in the DM, Salience (SAL),Cingulo-Opercular Task Control (COTC), Fronto-Parietal Task Control(FPTC), and Visual (VIS) networks among others.

The disclosed anxiety models also retained features across a widespreadset of networks including high representation in the DM network andsparser representation across executive networks (FPTC, COTC, DorsalAttention (DA)), SAL network, and sensory networks. Though conventionalresearch links anxiety to a set of networks including a threat circuit,the SAL, DM, and Attention networks, the models of the presentdisclosure indicate that the dysfunction is related to the DM, SAL, andSensory/Somatomotor networks, the FPTC network, in addition to COTC andVisual Attention (VA) networks. The disclosed models indicate that theunderlying elements of anxiety—difficulties regulating emotion infearful situations, detecting and controlling conflict, increasedattention to emotional stimuli—have relationships to this set ofnetworks.

The disclosed depression and mood models predicted outcome variablesthat were not as narrowly focused on a single symptom. TheMood/Dep_Hopkins sub score contained depressed mood questions but alsoones about guilt, suicide, loss of interest, and somatic concerns, whileMood_Bipolar contained questions about both depressed and manic moods,states which the brain may reflect differently. Both models relied on abroad set of networks beyond the negative affective circuit (ACC, mPFC,insula, and amygdala) previously proposed in the prior art. Bothanterior and posterior nodes of the DM network were informative to thedisclosed model as well as FPTC, COTC, Attention, SAL, and Sensorynetworks. Cognitive Control networks, Salience, and Attention, andAffective networks were involved in depressed mood while a central node,the subgenual cingulate, is involved in mood and connected within the DMnetwork.

The clinical scale feature categories, as used in the presentdisclosure, include items from the TCI, Hopkins Symptom Checklist, andthe Chapman scales across nearly all six symptom models. The TCI is aconsistently predictive scale, as used in the present disclosure, asassessed by a number of questions contributed for all six models ofmood, anhedonia, and anxiety. This scale measures temperaments such asharm avoidance and novelty seeking, which have previously beenassociated with depression and anxiety. In addition, the disclosedmodels picked out questions from TCI that pertained to social situationsas predictive of the social anhedonia severity. Therefore, theconsistent representation of TCI across the models suggests itspotential utility when screening patients for multiple symptom domains.

The relative contributions of the different feature types as providedfor by method 1600 above indicate that both scale items and fMRIconnectivity are highly important to model predictability. While scalefeatures tended to be more highly represented in the top 25% offeatures. Thus, their relative importance may be higher than fMRIfeatures, though the multimodal models performed better than scales-onlymodels, suggesting that both scale and fMRI components contain uniqueinformation. Such a comparison of different feature types intransdiagnostic or community-based psychiatric symptom severitybiomarker studies is not common in the prior art. Therefore, the presentdisclosure provides a valuable step when multiple data types areavailable for creating predictive models; each data type has benefitsand drawbacks in ease of collection, measurement stability, resourcesrequired for processing, etc. Different data types can be used accordingto these benefits and drawbacks.

Conventional research suggests that (1) sMRI regularly underperforms atMajor Depressive Disorder (MDD) diagnostic classification in comparisonto fMRI for MDD patients and (2) the lack of studies reporting sMRIabnormalities in SZ, BD, and ADHD reflects the lack of predictability orneed for larger sample sizes in detecting effects in this modality.

The present disclosure further examines the categorical origins of thefMRI features and clinical scale features for the disclosed models.Specifically, anhedonia models found not only elements of a rewardcircuit but also multiple nodes in the DM, Salience (SAL),Cingulo-Opercular Task Control (COTC), Fronto-Parietal Task Control(FPTC), and Visual (VIS) networks among others. Connectivity changestied to rewarding contexts in this wider set of networks have beenobserved in conventional research, while a meta-analysis of task-basedreward processing in MDD demonstrated dysfunctional activation in abroad set of regions including frontal, striatal, cerebellar, visual,and inferior temporal cortex. As nodes within the DM network areactivated both during self-referential processing and social andemotional processing, symptoms that decrease socially pleasurableexperiences could have bases in this network. Moreover, coordinationbetween several of these networks are necessary for healthy function,but patients with disruptions to the Salience network may have troubleswitching between DM and executive control networks, which may underlierumination or impaired reward processing. Indeed, subcortical nodes ofthe Salience network are located in mesocorticolimbic emotional andreward processing centers of the brain, so disruption of these functionsmay propagate to cortical salience regions and beyond.

The Mood_Bipolar outcome score contained questions about both depressedand manic moods, states which the brain may reflect differently.Conventional research found (1) increased amygdala-sensory connectivityand abnormal prefrontal-parietal connectivity during manic states, andextensive orbitofrontal to subcortical and cortical connectivity indepressed states and (2) the ratio of DM to sensory-motor networkactivity was greater in a depressed state of BD and less in manic statesin BD. The results of the disclosed models provide that a wide set ofregions and networks is linked to depressed and elevated mood;additionally, there may be some dissociation between the two with moreDM in depressed mood and sensory involvement in elevated mood. TheMood_Bipolar model includes multiple nodes from both sets of RSNs asimportant features.

Regarding the MRI features that may be indexing neurobiologicalmechanisms, the disclosed transdiagnostic regression approach isagnostic to the question of same/different mechanisms underlying thesesymptoms. It is likely that multiple mechanisms exist for each of thesesymptoms, but the modeled symptom constructs are based on sum scoresthat likely cannot differentiate different mechanisms (like anticipatoryv. consummatory anhedonia). In theory, differing mechanisms may evenspan diagnoses rather than differ between diagnoses. Regularizedregression modeling identifies all predictive features from a sample,and thus multiple possible mechanistic features, features reported asimportant for each model might not be related to a single underlyingmechanism; rather, the reported features may be related to multipleunderlying mechanisms. Models that incorporate multiple mechanisms canbe applicable to a wider population.

Experimental Method and Additional Details

An experimental methodology is disclosed further herein which providesadditional examples of methodologies 1500 and 1600, as would be readilyapparent to one skilled in the art. The experimental methodologyincludes experimental results which verify additional aspects of thedisclosed systems and methods; the experimental results further verifyadditional benefits of the present disclosure as compared againstconventional systems and methods.

Four groups of participants were included in the sample data, theparticipants drawn from adults aged 21-50 years: healthy controls (HC,n=130), individuals with schizophrenia (SZ, n=50), Bipolar Disorder (BD,n=49), or Attention Deficit and Hyperactivity Disorder (ADHD, n=43).This full set of participants is outlined below in Table 1.

TABLE 1 Demographic information for full set of participants HC SCZ BDADHD No. of participants 130 50 49 43 Age Mean age 31.26 36.46 35.1533.09 SD age 8.74 8.88 9.07 10.76 Range age 21-50 22-49 21-50 21-50Gender No. of female participants 62 12 21 22 Percent femaleparticipants 47.69% 24.00% 42.86% 51.16% Race American Indian or AlaskanNative 19.23% 22.00%  6.25%    0% Asian 15.38%  2.00%    0%  2.33%Black/African American  0.77%  4.00%  2.08%  2.33% White 78.46% 66.00%77.08% 88.37% More than one race    0%  2.00% 14.58%  6.98% Education Nohigh school  1.54% 18.00%  2.08%    0% High school 12.31% 44.00% 29.17%23.26% Some college 20.77% 18.00% 25.00% 30.23% Associate's degree 7.69%  4.00%  6.25%  6.98% Bachelor's degree 50.00% 10.00% 29.17%32.56% Graduate degree  6.92%    0%  4.17%  2.33% Other  0.77%  4.00% 4.17%  4.65% MRI Scanner No. of participants on scanner 1 106 25 26 23No. of participants on scanner 2 24 25 23 20

Comorbid diagnoses were allowed and identified for 81% of patients. Forthe three patient groups, stable medications were permitted. Diagnoseswere based on the Structured Clinical Interview for DSM-IV (SCID) andsupplemented with the Adult ADHD Interview. After examining subjects formissing data and performing quality control on the data (as detailedherein), the subject pool was reduced. Referring momentarily to FIG. 13,current medication usage status, grouped by medication class, isidentified for each group. Each bar represents the percent of adiagnostic group that was using a stable medication of that particularclass. Some diagnostic groups did not have any subjects usingmedications from a particular class. No HC subjects were on psychiatricmedications per enrollment criteria.

CNP Dataset

The CNP dataset (release 1.0.5), retrieved from the OpenNeuro platform,contains demographic, behavioral, clinical, and imaging data (no geneticdata is included). Of the extensive behavioral testing that participantsunderwent, the present disclosure provides analysis from tests ofparticipant self-reported symptoms and traits (clinician-administeredinstruments were only given to subsets of participants). Theself-reported scales used in our analysis include the Chapman socialanhedonia scale (denoted Chapsoc), Chapman physical anhedonia scale(Chapphy), Chapman perceptual aberrations scale (Chapper), Chapmanhypomanic personality scale, Hopkins symptom checklist (Hopkins),Temperament and Character Inventory (TCI), adult ADHD self-report scalev1.1 screener (ASRS), Barratt Impulsiveness Scale (Barratt), Dickmanfunctional and dysfunctional impulsivity scale (Dickman),multidimensional personality questionnaire—control subscale (MPQ),Eysenck's impulsiveness, venturesomeness, and empathy scale (Eysenck),scale for traits that increase risk for bipolar II disorder(Bipolar_ii), and Golden and Meehl's Seven MMPI items selected bytaxonomic method (Golden).

MRI Data Acquisition

The MM data acquired according to the experiments of the presentdisclosure were provided on 3T Siemens Trio scanners. Exemplary sMRIdata was T1-weighted and acquired using a magnetization-prepared rapidgradient-echo (MPRAGE) sequence with the following acquisitionparameters: TR=1.9 s, TE=2.26 ms, FOV=250 mm, matrix=256×256, 176 1-mmthick slices oriented along the sagittal plane. The resting-state fMRIscan was a single run lasting 304 s. The scan was acquired using aT2*-weighted echoplanar imaging (EPI) sequence using the followingparameters: 34 oblique slices, slice thickness=4 mm, TR=2 s, TE=30 ms,flip angle=90°, matrix size 64×64, FOV=192 mm. During the resting-statescan, subjects remained still and relaxed inside the scanner and kepttheir eyes open. No specific stimulus or task was presented to them.

Preprocessing Data into Features

Experiments conducted according to the present disclosure used responsesto individual questions from the thirteen (13) self-report scales asinput features for a total of five hundred seventy-eight (578)questions. One participant had incomplete clinical scale data and wasnot included in subsequent analyses. Outcome variables for modelingdysregulated mood, anhedonia, and anxiety were also selected from theclinical scales. Mood dysregulations included both depressed mood andmania.

Preprocessing of sMRI was performed using the recon-all processingpipeline from the FreeSurfer software package. The T1-weightedstructural image from each participant was intensity-normalized andskull-stripped. The subcortical structures, white matter, and ventricleswere segmented and labeled. The pial and white matter surfaces were thenextracted and tessellated, and cortical parcellation was obtained on thesurfaces according to a gyral-based anatomical atlas which partitionseach hemisphere into thirty-four (34) regions. The structural featuresfrom bilateral aparc.stats and aseg.stats files were extracted via theaparcstats2table and asegstats2table functions in FreeSurfer, and thisincluded cortical and subcortical regional volumes, cortical surfacearea, and cortical thickness estimates. Ten subjects had missing sMRIscans and were not included in subsequent analyses.

Preprocessing of rs-fMRI was performed using the AFNI software package.Preprocessing of each participant's echo planar image (EPI) dataincluded: removal of the first three volumes (before the scanner reachedequilibrium magnetization), de-spiking, registration of all volumes tothe now first volume, spatial smoothing with a 6 mm full-widthhalf-maximum Gaussian filter, and normalization of all EPI volumes bythe mean signal to represent data as percent signal change. Anatomicaldata also underwent several steps: deobliquing of the T1 data,uniformization of the T1 to remove shading artifacts, skull-stripping ofthe T1, spatial alignment of the T1 and FreeSurfer-segmented and-parceled anatomy to the first volume of the EPI data, and resampling ofthe FreeSurfer anatomy to the resolution of the EPI data. Subsequently,the ANATICOR procedure was used for nuisance tissue regression. Whitematter and ventricle masks were created and used to extract theblood-oxygen-level-dependent (BOLD) signals (before spatially-smoothingthe BOLD signal). A 25 mm-radius sphere at each voxel of the whitematter mask was used to get averaged local white matter signal estimateswhile the average ventricle signal was calculated from the wholeventricle mask. Time series for the motion estimates, and the BOLDsignals in the ventricles and white matter were detrended with afourth-order polynomial. To clean the BOLD signal, the experimentalmethodology provided for regressing out the nuisance tissue regressorsand the six motion estimate parameters. Cleaned data residuals were usedfor all subsequent analysis. Both the preprocessed T1 scan and thecleaned residuals of the EPI scan were warped to MNI space and resampledto 2 mm isotropic voxels. The time series of the cleaned residual datawas extracted from each of two hundred sixty-four (264) ROIs asdelineated by the Power atlas. At each ROI, the signals from the voxelswithin a 5 mm radius sphere were averaged. Pearson's correlations werethen calculated between the averaged time series from all ROIs yielding34,716 unique edges in the functional connectivity graph (upper triangleof the full correlation matrix). Ten additional subjects (beyond the tenwith missing sMRI data) did not have fMRI scans and were thus excludedfrom subsequent analysis.

Quality control (QC) for MRI preprocessing was performed individually onthe whole dataset by two authors (MM, YL) who had 85% and 89% agreementbetween them regarding rejection decisions for each participant's sMRIand rs-fMRI data, respectively. Specifically, participants were excludedif they had misregistration between fMRI and sMRI scans, >3 mm headmotion in the fMRI scan (to correspond with edge length of a voxel infunctional scan), headphone artifacts that overlapped with brain tissuein the sMRI scan, incorrect FreeSurfer-automated grey/white segmentationand anatomical parcellation in the sMRI scan, and aliasing or field ofview artifacts in either scan. Discrepancies were resolved between thetwo authors in order to create a final rejection list of participants.The disclosed methodology used two hundred seventy (270) sMRI featuresfrom FreeSurfer-calculated cortical and subcortical regional volumes,cortical surface area, and cortical thickness estimates, and 34,716AFNI-processed fMRI connectivity features calculated from pairwisePearson's correlations between two hundred sixty-four (264) ROIs of thePower atlas. Subsets of these input features were used as predictorvariables in subsequent modeling as explained below.

Output variables that were modeled included those which indexeddepression, anxiety, anhedonia, or other negative symptoms. A mix oftotal scores, sub-scale sum or average scores, and individual questionscores were predicted as each has their advantages. These scores includethe twenty-eight (28)-question versions of the total HAMD score(‘hamd’), the HAMD subscore for questions 1, 7, and 8 (‘hamd178’,indexes a melancholic-type of symptom), the HAMD item score for question7 (‘hamd7’, indexes lack of interest or anhedonia), the Chapman SocialAnhedonia total score (‘chapsoc’), the Chapman Physical Anhedonia totalscore (‘chapphy’), BPRS negative subscore (‘bprs_negative’, the averageof negative symptom questions 13, 16, 17, and 18), BPRSdepression-anxiety subscore (‘bprs_depanx’, the average of depressionand anxiety symptom questions 2, 3, 4, and 5), Hopkins anxiety score(‘hopkins_anxiety’, the average of anxiety symptom questions 2, 17, 23,33, 39, and 50), Hopkins depression score (‘hopkins_depression’, theaverage of depression symptom questions 5, 15, 19, 20, 22, 26, 29, 30,31, 32, and 54), Bipolar ii mood score (‘bipolarii_mood’, the sum ofmood questions 1-9), Bipolar ii anxiety score (‘bipolar_anxiety’, thesum of anxiety questions 24-31), SANS anhedonia factor score(‘sans_factor_anhedonia’, the average of anhedonia questions 17, 18, 19,and 20), SANS anhedonia global score (‘sans_global_anhedonia’, questions21 which is the clinician's overall anhedonia assessment score), SANSavolition factor score (‘sans_factor_avolition’, the average ofavolition items 12, 13, 14, and 15), SANS avolition global score(‘sans_globals_avolition’, question 16 which is the clinician's overallavolition assessment score), SANS blunt affect factor score(‘sans_factor_bluntaffecct’, the average of affective flattening items1, 2, 3, 4, 5, and 6), SANS blunt affect global score(‘sans_global_bluntaffect’, question 7 which is the clinician's overallblunt affect assessment score), SANS alogia factor score(‘sans_factor_alogia’, the average of alogia items 8, 9, and 10), SANSalogia global score (‘sans_global_alogia’, question 11 which is theclinician's overall alogia assessment score), SANS attention factorscore (‘sans_factor_attention’, the average of attention items 22 and23), and SANS attention global score (‘sans_global_attention’, question24 which is the clinician's overall attention assessment score).

Sum scores are commonly accepted by the FDA regarding positive efficacyresults, but using only sum scores may obfuscate brain-behaviorrelationships at more fine-grained levels of symptoms. Subjects withmissing values (“n/a”) for any input or output variables or who did notpass MRI QC were removed from the input set. As different input featuresets were used, different models had different sample sizes. Theavailability of clinical scores for particular clinical scales takenonly by certain subsets of patients also affected the final sample sizefor each model. See the samples sizes resulting from these factors inTable 2.

TABLE 2 Sample Sizes Resulting from Select Factors Scales_ Scales_ sMRI_Scales_ sMRI_ Predicted Scores Scales sMRI fMRI sMRI fMRI fMRI fMRIChapman Social Anhedonia 271 206 147 205 117 146 116 Chapman Physical271 206 147 205 117 146 116 Anhedonia HAMD, total score 141 108 82 10763 81 62 HAMD, q1, 7, 8 sum score 140 108 82 107 63 81 62 HAMD, q7 140108 82 107 63 81 62 BPRS, negative score 141 108 82 107 63 81 62 BPRS,depression-anxiety 141 108 82 107 63 81 62 score Hopkins, anxiety score271 206 147 205 117 146 116 Hopkins, depression score 271 206 147 205117 146 116 Bipolar II, depression score 271 206 147 205 117 146 116Bipolar II, anxiety score 271 206 147 205 117 146 116 SANS, anhedoniafactor 99 75 54 74 40 53 39 SANS, avolition factor 99 75 54 74 40 53 39score SANS, blunt affect factor 99 75 54 74 40 53 39 score SANS, alogiafactor score 99 75 54 74 40 53 39 SANS, attention factor 99 75 54 74 4053 39 score SANS, anhedonia global 87 74 83 73 39 52 38 score SANS,avolition global 99 75 54 74 40 53 39 score SANS, blunt affect global 9975 54 74 40 53 39 score SANS, alogia global score 99 75 54 74 40 53 39SANS, attention global 99 75 54 74 40 53 39 scoreRegression Modeling

All regression modeling was performed with a combination of Pythonlanguage code and the Python language toolbox scikit-learn(http://scikit-learn.org/stable/index.html). The disclosed experimentmodeled six different symptom severity scores across the clinicalscales, two each for mood, anhedonia, and anxiety. All outcome measureswere precalculated in the CNP dataset. For mood, the average of theHopkins scale depression symptom questions was used (further referencedas Mood/Dep_Hopkins) and the sum of mood questions from the Bipolar_iiinventory (Mood_Bipolar). The two anhedonia variables were derived fromtotal scores on the Chapman Social Anhedonia scale (Anhedonia_Chapsoc)and the Chapman Physical Anhedonia scale (Anhedonia_Chapphy). Anxietywas indexed from the sum of Bipolar_ii anxiety questions(Anxiety_Bipolar) and average of anxiety symptom questions(Anxiety_Hopkins).

The experimental methodology built one hundred twenty-six (126)—(6outcome variables×7 predictor variable sets×3 model algorithms)—sets ofmodels. For each of these sets of models, hyperparameters were tunedusing 5-fold cross-validated grid-search on a training set of data (80%of data), and the model using the selected hyperparameters was tested ona separate evaluation set of data (20% held-out sample). Thistrain/evaluation split was performed twenty-five (25) times for aversion of nested cross-validation where the outer loop was repeatedrandom sub-sampling validation. Critically, this nested cross-validationapproach means that models are trained on a training set that iscompletely separate from an evaluation set used to generate evaluationmetrics reported as final results. For each of the 126 sets of models,the experimental methodology took an importance-weighted, forwardselection approach to regression modeling, involving three main steps:first, an initial rank-ordering step for ordering features byimportance; second, a forward-selection search step for building aseries of models utilizing growing subsets of ordered features (i.e.,the best features) selected from the first step; and third, anevaluation step to choose the best model and subset of featuresaccording to a prespecified criterion to find the optimal model.

The twenty-five (25) iterations of training/evaluation set splits formodeling and validation as explained above allowed generation ofdescriptive statistics for each feature subset to calculate median andstandard deviation metric scores. The metrics chosen for the final stepof evaluation were mean squared error (MSE) and r² calculated on theheld-out evaluation sets. The median r² and standard deviation of r²were found for each subset. And the “best model” overall was selected byfinding the maximum median r² value over all feature subsets andselecting the model that corresponded to that max median r² value (FIGS.1A-1B). To find which input feature set and which model type led to thebest biomarkers, subsequent comparisons were also made based on the r²of the best models.

Thus, further examination of features focused on this set of models. Aninitial comparison between models that used the full feature sets (up to35,564 features) and those that used the optimal set of truncatedfeatures (ordered subsets of the full feature sets identified throughthe forward modeling approach) demonstrated vastly differentperformances between the modeling approaches. The modeling results forElastic Net using the full feature sets on average explained 22% of thevariance while truncated sets explained an average of 78% for theclinical scales data, sMRI data and fMRI data input models (metrics forfull features sets are presented in below in Table 3).

TABLE 3 Comparison of all Elastic Net Models using Full Feature Sets.Full Features Sets Feature Set Scales_ Outcome Scales_ sMRI_ Scales_sMRI_ Variables Metric Scales sMRI fMRI sMRI fMRI fMRI fMRI Mood/Dep_median MSE 0.196 0.310 0.276 0.208 0.349 0.224 0.103 Hopkins median r²0.373 0.019 −0.595 0.229 −0.157 0.290 0.238 Mood_ median MSE 2.17211.538 7.683 1.809 6.379 2.426 2.099 Bipolar median r² 0.670 −0.282−0.140 0.676 −0.255 0.579 0.658 Anhedonia_ median MSE 48.835 69.99264.141 36.425 65.951 25.679 59.355 Chapphy median r² 0.249 −1.046 −0.1560.052 −0.537 0.435 0.247 Anhedonia_ median MSE 23.597 118.983 60.53621.494 39.454 25.938 25.641 Chapsoc median r² 0.539 −0.941 −0.255 0.597−0.443 0.565 0.293 Anxiety_ median MSE 0.105 0.328 0.343 0.130 0.2610.253 0.213 Hopkins median r² 0.324 −0.028 −0.110 0.322 0.031 −0.097−0.510 Anxiety_ median MSE 1.831 3.432 3.634 1.815 2.762 1.200 1.108Bipolar median r² 0.183 −0.115 0.003 0.624 −0.072 0.473 0.387

For the six models using Elastic Net with the clinical scales data, sMRIdata, and fMRI data input feature set, model performance was evaluatedon the held-out evaluation set with measured v. predicted plots (FIGS.2A-2C) and r² values across models for different outcome variables (seeTable 4 below, last column). All six models were highly predictive withthe variance explained ranging from 65-90% and number of non-zerofeatures p ranging from 28-106 (Mood/Dep_Hopkins r²=0.72, p=28;Mood_Bipolar r²=0.90, p=93; Anhedonia_Chapphy r²=0.65, p=32;Anhedonia_Chapsoc r²=0.80, p=106; Anxiety_Hopkins r²=0.75, p=47;Anxiety_Bipolar r²=0.85 p=31).

TABLE 4 Comparison of all Elastic Net Models using Truncated FeatureSets Returned by Forward Selection Approach Truncated Features SetsFeature Set Scales_ Outcome Scales_ sMRI_ Scales_ sMRI_ Variables MetricScales sMRI fMRI sMRI fMRI fMRI fMRI Mood/DEP_ median MSE 0.148 0.2990.159 0.106 0.138 0.110 0.076 Hopkins median r² 0.530 0.019 0.450 0.6770.415 0.610 0.721 p 50 3 29 102 16 26 28 Mood_Bipolar median MSE 1.1836.408 2.530 1.021 1.867 0.814 0.614 median r² 0.836 0.123 0.625 0.8640.719 0.874 0.904 p 112 22 255 114 241 236 93 Anhedonia_ median MSE19.670 42.835 21.406 15.178 14.419 9.648 15.814 Chapphy median r² 0.6420.158 0.656 0.690 0.740 0.841 0.652 p 240 61 358 123 211 211 32Anhedonia_ median MSE 12.510 43.893 13.246 10.847 14.682 8.627 9.886Chapsoc median r² 0.782 0.065 0.732 0.796 0.677 0.829 0.804 p 126 32 34563 559 31 106 Anxiety_ median MSE 0.134 0.260 0.110 0.145 0.098 0.0950.077 Hopkins median r² 0.525 0.042 0.653 0.471 0.650 0.704 0.751 p 54 685 49 29 127 47 Anxiety_ median MSE 1.252 2.865 0.988 0.798 0.703 0.5890.535 Bipolar median r² 0.616 0.121 0.644 0.735 0.789 0.825 0.847 p 6216 153 58 161 32 31

Next, the proportions of features derived from clinical scale data, fMRIdata, and sMRI data feature sets were compared for the best model foreach outcome variable both among the whole feature set and the top 25%of features (FIGS. 3A-3B). The best models for Mood/Dep_Hopkins,Anhedonia_Chapphy, and Anxiety_Bipolar had a roughly equal number ofclinical scale and fMRI features while Anxiety_Hopkins,Anhedonia_Chapsoc, and Mood_Bipolar models had a bias towards fMRIfeatures (FIG. 3A). FIG. 3B shows, however, that for many outcomevariables there was a disproportionate number of scale features in thetop features. Notably, there was a paucity of sMRI features in boththese models as only Anhedonia_Chapphy had any sMRI features selected bythe models.

The disclosed experimental methodology modeled six different symptomseverity scores across the clinical scales, two each for mood,anhedonia, and anxiety. First, the methodology provides for predicting amix of total scores and sub-scale sum or average scores from scales thatwere given to all three patient groups and HCs to retain the largestnumber of participants possible in the models. Each of these scores wasalready calculated and included in the CNP dataset. For mood, theaverage of depression symptom questions 5, 15, 19, 20, 22, 26, 29, 30,31, 32, and 54 from the Hopkins inventory (precalculated“Hopkins_depression” score, further referenced as Mood/Dep_Hopkins inthis study) and the sum of mood questions 1-9 from the Bipolar_iiinventory (precalculated “Bipolar_mood” score, further referenced asMood_Bipolar in this study) was used. The two anhedonia variables werederived from total scores on the Chapman Social Anhedonia scale(precalculated “Chapsoc” score, further referenced as Anhedonia_Chapsocin this study) and the Chapman Physical Anhedonia scale (precalculated“Chapphy” score, further referenced as Anhedonia_Chapphy in this study).And anxiety was indexed from the sum of Bipolar_ii anxiety questions24-31 (precalculated “Bipolar_anxiety” score, further referenced asAnxiety_Bipolar in this study) and average of anxiety symptom questions2, 17, 23, 33, 39, and 50 from the Hopkins anxiety score (precalculated“Hopkins_anxiety” score, further referenced as Anxiety_Hopkins in thisstudy).

For each of the six models (Mood/Dep_Hopkins, Mood_Bipolar,Anhedonia_Chapsoc, Anhedonia_Chapphy, Anxiety_Bipolar, Anxiety_Hopkins),seven combinations of feature types were used as inputs to be able toevaluate the performance of single and multimodal feature sets. Theseincluded (1) clinical scales data only, (2) sMRI data only, (3) fMRIdata only, (4) clinical scales data and sMRI data, (5) clinical scalesdata and fMRI data, (6) sMRI data and fMRI data, and (7) clinical scalesdata, sMRI data, and fMRI data. As different input feature sets wereused, different models had different sample sizes. The samples sizesresulting from this factor were n=271 for the clinical scales onlymodels, n=206 for sMRI only models, n=147 for fMRI only models, n=205for clinical scales data and sMRI data models, n=117 for sMRI data andfMRI models, n=146 for clinical scales data and fMRI data models, andn=116 for clinical scales data, sMRI data, and fMRI data models (seeTable 5 below. As input features varied in their mean values andregularized models require normally distributed data, each input featurewas scaled separately to have zero mean and unit variance. This approachof using clinical scales, sMRI, and fMRI features as inputs reflectmultiple goals of the disclosed methodology: (1) finding the feature setthat maximizes predictability, and (2) exploring ways to reduce thedimensionality of feature sets. For example, in the case of usingclinical self-report measures to predict clinical scale measures,reducing the dimensionality is useful in removing redundancy and findinga compact, optimized set of questions which could reduce time and/orcost of administration and which could potentially better map ontoneural circuitry.

TABLE 5 Sample size for each model Number of Subjects Scales_ OutcomeScales_ sMRI_ Scales_ sMRI_ Variables Scales sMRI fMRI sMRI fMRI fMRIfMRI Mood/Dep_ 271 206 147 205 117 146 116 Hopkins Mood_ 271 206 147 205117 146 116 Bipolar Anhedonia_ 271 206 147 205 117 146 116 ChapphyAnhedonia_ 271 206 147 205 117 146 116 Chapsoc Anxiety_ 271 206 147 205117 146 116 Hopkins Anxiety_ 271 206 147 205 117 146 116 Bipolar

In order to probe performance with a variety of modeling algorithms, foreach scale output and feature set input, two regularized general linearmodel regression algorithms, LASSO and Elastic Net, and one non-linearregression model algorithm, Random Forest, were used for the modeling.These methods improved prediction accuracy and interpretability overregular regression methods using ordinary least squares. LASSO usesregularization by imposing an L1-penalty parameter to force somecoefficients to zero; this step introduces model parsimony that benefitsinterpretability and predictive performance while guarding againstoverfitting. If predictor variables are correlated, however, the LASSOapproach arbitrarily forces only a subset of the variables to zero,which makes interpretation of specific features more difficult. TheElastic Net algorithm uses both L1- and L2-penalty parameters to betterretain groups of correlated predictor variables; this improvesinterpretability as highly predictive features will not randomly be setto zero (thereby diminishing their importance to the model). It is alsobetter suited in cases when the number of predictor variables is muchgreater than the number of samples (p>>n). The non-linear regressionalgorithm Random Forest was also chosen for comparison purposes.

In the one hundred twenty-six (126) sets of models built according tothe present disclosure, hyperparameters were tuned using 5-foldcross-validated grid-search on a training set of data (80% of data), andselected hyperparameters were used on a separate evaluation set of data(20% held-out sample). This train/evaluation split was performedtwenty-five times (25×) for a version of nested cross-validation (innerloop is 5-fold for hyperparameter optimization and model fitting, andouter loop is repeated random sub-sampling validation twenty-five times(25×) for model evaluation). This nested cross-validation approach meansthat models are trained on a training set that is completely separatefrom an evaluation set used to generate evaluation metrics reported asfinal results. The approaches of nested cross-validation and ofsplitting data between training and evaluation data is one way tominimize overfitting in addition with permutation testing (which canalso be performed). The hyperparameter range for LASSO was alpha equalto 0.01, 0.03, and 0.1 (three samples through the log space between 0.01and 0.1) which is the coefficient of the L1 term. Hyperparameter rangesfor Elastic Net were alpha equal to 0.01, 0.03, and 0.1, and 11 ratioequal to 0.1, 0.5, and 0.9 which is the mixing parameter used tocalculate both L1 and L2 terms. Hyperparameter ranges for Random Forestincluded the number of estimators equal to 10 or 100 and the minimumsamples at a leaf equal to 1, 5, and 10. The best hyperparameters werechosen from the model that maximized an interim r² score (coefficient ofdetermination) across the 5-fold cross-validation procedure in thetraining set and applied to the model of the never-seen evaluation set.

For each of the one hundred twenty-six (126) sets of models, animportance-weighted, forward selection approach to regression modeling(a variation of forward-stepwise selection) was applied as a data-drivenway to identify the optimal feature subset to include in regressionmodeling. Finding an optimal subset helps in high-dimensional caseswhere the number of features is greater than the number of samples toavoid overfitting of the models. It also reduces nuisances fromuninformative input variables without requiring the modeler to decide apriori whether a variable is signal or noise. This approach involvesthree main steps: (1) an initial rank-ordering step for orderingfeatures by importance; (2) a forward-selection search step for buildinga series of models utilizing growing subsets of ordered features (i.e.,the best features) selected from the first step; and (3) an evaluationstep to choose the best model and subset of features according to aprespecified criterion to find the optimal model. This approach thusintegrates feature selection into modeling using a multivariate embeddedmethod that can take variable interactions into account to potentiallyconstruct more accurate models. Within each step, each new modelutilized the training/evaluation set split and grid-search procedure tooptimize hyperparameters as explained above. First, the featurerank-ordering step uses the full feature set (either clinical scalesdata only, sMRI data only, etc.) as the input to the model algorithmswhich returns not only predicted values for the evaluation dataset butalso the importance of each feature for the resulting model. Featureimportance was assessed from the regression coefficients of LASSO andElastic Net models with ordering (most important to least important)based on the absolute value of the coefficient. Ordering by absolutevalue reflects that features with the largest magnitude influence thesymptom severity scores the most. Feature ordering for the Random Forestalgorithm (typical regression coefficients are not available) was doneusing the “gini importance” or mean decrease in impurity as implementedin the scikit-learn library.

Second, the forward-selection search step systematically searchesthrough subsets of the rank-ordered features (truncated feature sets)for the subset that leads to the best model. Since having more featuresthan samples (p>>n) both increases the risk of overfitting and decreasesthe performance due to uninformative features adding nuisances, thisdata-driven way of searching the ordered feature space for an optimalsubset of features was used. A series of regressions on subsets of theordered features was run with subsets chosen in powers of two (i.e.,inputting the top feature only, the top two features only, the top fourfeatures only, etc.) up to two hundred fifteen (215) features. The outerloop of nested cross-validation (the twenty-five (25) iterations oftraining/evaluation set splits for modeling and validation as explainedabove) also allowed generation of descriptive statistics for eachfeature subset to get median and standard deviation metric scores. Themetrics chosen for the final step of evaluation were mean squared error(MSE) and r² calculated on the held-out evaluation sets. The median r²and standard deviation of r² were found for each subset. And the “bestmodel” overall was selected by finding the maximum median r² value overall feature subsets and selecting the model that corresponded to thatmax median r² value (FIGS. 1A-1B). All subsequent follow-up is on theone hundred twenty-six (126) best models for each combination ofinput/model type/output.

To find which input feature set (clinical scales data only, sMRI dataonly, fMRI data only, clinical scales data and sMRI data, clinicalscales data and fMRI data, sMRI data and fMRI data, and clinical scalesdata, sMRI, and fMRI) and which model type (LASSO, Elastic Net, RandomForest) led to the best biomarkers, subsequent comparisons were alsomade based on the r² of the best models. The r² is a standardizedmeasurement of explained variance (with a maximum value of 1 but anunbounded minimum) while the MSE values are not standardized across thedifferent models making it less appropriate to use MSE for comparison.

Several control scenarios were implemented to test alternativehypotheses that modeling may have been impacted by overfitting orvariables of no interest. Model performance for the best models (chosenby the methods above) was compared with (1) models with permuted outcomevariables (to test for overfitting) and (2) models that includedvariables of no interest in addition to the features of interest. In thefirst case, the null hypothesis is that the features and severity scoresare independent; however, an overfit model could misidentify dependence.But if the high performance of the disclosed models is due toidentification of real structure in the data rather than overfitting,the best models will perform significantly better than models built fromthe permuted data and the null hypothesis can be rejected. After theoriginal ordering of features and selection of the 2n subset that led tothe best model, severity scores were permuted across participants for agiven outcome variable one hundred (100) times, and one hundred (100)models were built based on the permuted scores.

Predictability (assessed with r²) was calculated from these one hundred(100)-permuted models which allowed generation of an empirically-deriveddistribution of r² values for calculating a test statistic (p-value)compared to the median r² of the chosen best model. In the secondcontrol case, models built with predictor variables of no interestallowed assessment of the predictability of these variables in relationto predictors of interest (scales data, sMRI data, fMRI data) to see ifpossible confounding variables drive the results. Variables of nointerest included age; gender; years of schooling; in-scanner meanframewise displacement (FD) which was calculated as an L2 norm; the sixin-scanner motion measures for x, y, z directions and pitch, roll, yaw;sharp head motion (output of AFNI's @1dDiffMag); number of frames thatwould have been censored at a threshold of FD>0.5 mm; the current use ofmedication categorized by medication class; and scanner number (sincetwo scanners were used). Six sets of models were generated for the sixoutcome variables also using the importance-weighted forward-selectionapproach. The r² score distribution from the best of the nuisance models(p that optimized median r²) was compared to the best models withoutnuisance variables included with the non-parametric Wilcoxon rank-sumtests to assess if nuisance variables change the predictive ability ofthe models.

Feature Stability

Feature stability (a measure of the consistency of feature weightings)was calculated using a correlation approach for the best models. ThePearson correlation coefficient between predictor variable coefficients(i.e., the importance measurements) retains more information overfeature rankings or include/exclude subsets. From the 25 subsamplings ofsubjects into training and evaluation sets for each subset of features(p=1, 2, 4, 8, . . . , 32768 during the forward-selection search step),feature stability was calculated across these twenty-five (25)iterations for both the 32768 set of features (as it is nearly the fullset of 35564 features) and the best models (the ones with the optimal pfeatures as found from the best median r²). Thus, given the vector ofpredictor coefficients for each of the twenty-five (25) modeliterations, the pairwise correlations can be calculated between thetwenty-five (25) coefficient vectors (25*(25−1)/2=300 combinations)which gives three hundred (300) pairwise correlations for each featuresubset. The mean of these three hundred (300) correlation coefficientscan be taken for each feature subset to find these correlationcoefficient means.

FIG. 9 shows the feature stability for the Elastic Net scales+sMRI+fMRImodels. For the set of 32768 features, the correlation coefficient meanswere in a moderate range of 0.4 to 0.61 for Anxiety_Hopkins andMood/Dep_Hopkins, respectively (shown in FIG. 9). In contrast, thefeature stability of the models with optimal p features (as found fromthe best median r²) range from 0.8 to 0.93 for Anhedonia_Chapsoc andMood/Dep_Hopkins, respectively. So feature stability is moderate whenusing the nearly full set of features but improves when using an optimalsubset selected with the forward modeling approach.

Clinical Features Associated with Symptom Severity

The disclosed methodology further examined groupings of the scale-basedfeatures sorted by the proportion of the scales from which they arederived. For each model, the scale features for the best model wereproportionately selected from the scales shown in FIGS. 4A-4C. The TCIscale, in particular, was highly represented compared to the otherscales in all six models (note that Hopkins, Bipolar, Chapphy, andChapsoc items were not be included in all models). TCI contained anumber of questions on temperament and character traits that could berelated to a variety of symptoms, and the disclosed results suggestedthat it contained questions that are predictive of mood, anhedonia, andanxiety (shown in Table 6 below). Regression coefficients (ordered bymagnitude) are either positive or negative, indicating that a “True”answer for the respective question increased or decreased the outcomevariable score, respectively.

TABLE 6 Predictive Temperament and Character Inventory (TCI) questionsfor models of mood, anhedonia, and anxiety. CNP Regression QuestionOutcome Variable Coefficient Label True/False Question Mood/Dep_Hopkins0.06 tci149t I often stop what I am doing because I get worried, evenwhen my friends tell me everything will go well. −0.05 tci76p I am morehard-working than most people. 0.04 tci92t I need much extra rest,support, or reassurance to recover from minor illnesses or stress. 0.02tci22t I have less energy and get tired more quickly than most people.−0.01 tci210t People find it easy to come to me for help, sympathy, andwarm understanding Mood_Bipolar 0.17 tci140p I often give up on a job ifit takes much longer than I thought it would. 0.07 tci12t I often feeltense and worried in unfamiliar situations, even when others feel thereis little to worry about. 0.06 tci81t Usually I am more worried thanmost people that something might go wrong in the future. 0.05 tci217t Iusually feel tense and worried when I have to do something new andunfamiliar. 0.04 tci53t I lose my temper more quickly than most people.Anhedonia_Chapphy 0.77 tci217t I usually feel tense and worried when Ihave to do something new and unfamiliar. −0.52 tci5p I like a challengebetter than easy jobs. 0.52 tci156t I don't go out of my way to pleaseother people. 0.47 tci120t I find sad songs and movies pretty boring.0.25 tci83t I feel it is more important to be sympathetic andunderstanding of other people than to be practical and tough-minded.Anhedonia_Chapsoc −1.19 tci117t I would like to have warm and closefriends with me most of the time. 1.18 tci231t I usually stay away fromsocial situations where I would have to meet strangers, even if I amassured that they will be friendly. −0.79 tci21t I like to discuss myexperiences and feelings openly with friends instead of keeping them tomyself 0.57 tci44t It wouldn't bother me to be alone all the time. 0.55tci46t I don't care very much whether other people like me or the way Ido things. −0.48 tci210t People find it easy to come to me for help,sympathy, and warm understanding 0.37 tci201t Even when I am withfriends, I prefer not to \“open up\” very much. 0.34 tci180t I usuallylike to stay cool and detached from other people. 0.14 tci70t I like tostay at home better than to travel or explore new places.Anxiety_Hopkins 0.05 tci141t Even when most people feel it is notimportant, I often insist on things being done in a strict and orderlyway. 0.05 tci27t I often avoid meeting strangers because I lackconfidence with people I do not know. 0.04 tci180t I usually like tostay cool and detached from other people. Anxiety_Bipolar −0.25 tci157tI am not shy with strangers at all. 0.25 tci54t When I have to meet agroup of strangers, I am more shy than most people. 0.23 tci81t UsuallyI am more worried than most people that something might go wrong in thefuture. 0.16 tci129t I often feel tense and worried in unfamiliarsituations, even when others feel there is no danger at all. −0.14 tci3tI am often moved deeply by a fine speech or poetry. 0.10 tci211t I amslower than most people to get excited about new ideas and activities.

TCI was a scale with important predictive features for all six models.It contained a number of questions on temperament and character traitsthat could be related to a variety of symptoms, and the resultssuggested that it contained questions that are predictive of mood,anhedonia, and anxiety, as shown above in Table 5. For example, 43% ofthe questions predictive of Anxiety_Bipolar were from TCI, with the mostpredictive question being, “I am not shy with strangers at all.”Positive responses to this question predicted a lower Anxiety_Bipolarscore since the regression coefficient was negative in this model.Though not uniformly so, some of the other questions also assessedshyness or worry. The Anhedonia_Chapsoc model also had a very highpercentage of TCI questions, with the most predictive question being, “Iwould like to have warm and close friends with me most of the time.”Here positive responses indicated decreased social anhedonia severity asthe regression coefficient was also negative. While not all questions inthe TCI pertain to people and social situations, all but one of theremaining questions that were predictive of the Anhedonia_Chapsoc scoredid include mention of these situations. The predictive questions forthe Anhedonia_Chapphy, Mood/Dep_Hopkins, Mood_Bipolar, andAnxiety_Hopkins scores were more mixed overall, though. Additionally,FIGS. 4A-4C show that Chaphyp questions were also predictive in allmodels (but only contributed 1-2 items in five of the six scales). Themost numerous questions (6/31) from Chaphyp were for Mood_Bipolar whichmay be expected as the Chapman hypomanic scale, and the Mood_Bipolarsubscore of this scale both included an assessment of mania (as opposedto the Mood/Dep_Hopkins score which is more related to depressed moodand depressive symptoms).

Neurobiological Characteristics of Dysregulated Mood, Anhedonia, andAnxiety

The fMRI connectivity features were composed of the strengths of networkedges (connections between nodes) but can also be grouped by suggestedintrinsic resting-state networks from the Power atlas. As the number offMRI connectivity features selected by the models were a small subset ofall possible fMRI connectivity features, full connectivity matrices arequite sparse (the full connectivity matrices are shown in FIGS.10A-10C). Therefore, the number of edges within and between eachintrinsic resting-state network (RSN) is shown in the connectivitymatrices of FIGS. 5A-5F for each outcome variable. The predictive fMRIconnectivity features appeared mostly distributed across multiplenetworks rather than selective to a few particular networks (FIGS.5A-5F). Connectivity features implicate nodes in ten RSNs forMood/Dep_Hopkins, 12 RSNs for Mood_Bipolar, 10 RSNs forAnhedonia_Chapphy, 12 RSNs for Anhedonia_Chapsoc, 13 RSNs forAnxiety_Hopkins, and 10 RSNs for Anxiety_Bipolar models (out of fourteen(14) RSNs from this atlas). While Anhedonia_Chapsoc connectivitymeasures were also distributed, there was a higher concentration ofconnectivity features between the Default Mode (DM) network and othernetworks. In particular, the predictive edges between the DM and othernetworks mostly originate from the anterior cingulate and/or the medialorbitofrontal lobe. The Anhedonia_Chapsoc model also contained nodes inthe top five features that were located within a reward circuitincluding the putamen and orbitofrontal cortex (OFC). Edges eitherwithin the DM network or between the DM and other networks consistentlywere the most numerous features relative to all other within- andbetween-network features across all models. All the features in eachmodel, including sMRI, are available upon request from the authors.

Control models, including nuisance variables, found no predictiveadvantage of motion, age, gender, years of schooling, scanner number, ormedication usage with the exception of SNRI antidepressants onAnhedonia_Chapphy models.

Controls and Additional Models

To evaluate whether the disclosed models are influenced by confoundingdemographic or other variables, for each of the six outcome variables,six Elastic Net models were built with the Scales+sMRI+fMRI+nuisancefeatures; the distribution of 25 r² measures was selected for the bestnuisance models (p that optimized r²) to compare with the r² measures ofthe best Scales+sMRI+fMRI non-nuisance models. Two comparisons returnedno difference (Mood/Dep_Hopkins: Scales+sMRI+fMRI median r²=0.72,Scales+sMRI+fMRI+nuisance median r²=0.69, Wilcoxon Rank Sum Ustatistic=296, p=0.38; Mood_Bipolar: Scales+sMRI+fMRI median r²=0.90,Scales+sMRI+fMRI+nuisance median r²=0.90, U statistic=305, p=0.45). Twocomparisons show significantly improved performance of theScales+sMRI+fMRI models (Anxiety_Bipolar: Scales+sMRI+fMRI medianr²=0.85, Scales+sMRI+fMRI+nuisance median r²=0.76, U statistic=135,p=0.0003; Anxiety_Hopkins: Scales+sMRI+fMRI median r²=0.75,Scales+sMRI+fMRI+nuisance median r²=0.59, U statistic=128, p=0.0002).And two comparisons show significantly improved performance of theScales+sMRI+fMRI+nuisance models (Anhedonia_Chapsoc: Scales+sMRI+fMRImedian r²=0.80, Scales+sMRI+fMRI+nuisance median r²=0.83, Ustatistic=221, p=0.038; Anhedonia_Chapphy: Scales+sMRI+fMRI medianr²=0.65, Scales+sMRI+fMRI+nuisance median r²=0.83, U statistic=94,p<0.0001). Critically, though Scales+sMRI+fMRI+nuisance models forAnhedonia_Chapsoc and Anhedonia_Chapphy outcomes performed better, noneof the nuisance variables were actually selected in models (they all hadcoefficients equal to zero) except for “Antidepressant-SNRI” in theAnhedonia_Chapphy model (it was ranked 22 out of 207 non-zero features).Thus, current usage of SNRI antidepressants may affect physicalanhedonia severity, but none of the other measured confounding variablesaffect the disclosed models.

Additionally, another set of Elastic Net models with theScales+sMRI+fMRI feature set but with scrambled severity scores (apermutation testing approach) was built and used to test foroverfitting, but no evidence of overfitting was found using thisapproach as demonstrated by the empirical null distributions (shown inFIGS. 11A-11C). For all six models, the median r² of the best models wasstatistically significant (p<0.01).

Methodology Using Resting-State fMRI Data

Uncovering the biological basis of patient heterogeneity is a key tocreating clinically relevant biomarkers. Non-invasive imaging enablesthe visualization of the brain-to-symptom links underlyingneurobehavioral disorders. Conventional technology is often too narrowlyapplied by only examining one aspect of biology (e.g., anatomical orfunctional MM measures) or a single diagnostic group. Since symptomssuch as depression span multiple neurobehavioral disorders, a morerobust symptom biomarker can be better captured by examiningtransdiagnostic patient cohorts and utilizing multiple neuroimagingmodalities.

Therefore, embodiments of the present disclosure provide atransdiagnostic multimodal MRI model that successfully identifiedbiomarkers that can reliably predict clinician-rated depression severityacross multiple neurobehavioral disorders. This model can use theConsortium for Neuropsychiatric Phenomics dataset, which includesresting-state functional MRI (rs-fMRI) and structural-MM (sMRI) imagingmeasures from patients with schizophrenia, bipolar disorder, andattention deficit and hyperactivity disorder (n=142 total). Inputfeatures included preprocessed sMRI volume, surface, and thicknessmeasures (270 features) and preprocessed rs-fMRI connectivity measures(34,716 features). The model provides an outcome measure of depressionin the clinician-rated 28-item total score from the Hamilton RatingScale for Depression (HAMD). The disclosed model also used animportance-ranked forward selection procedure with Elastic Netregression and cross-validation for an efficient, data-driven featureselection approach to identify the most predictive features from thesehigh-dimensional data. This data-driven approach yielded a highlypredictive transdiagnostic model that explained 61% of variance of theHAMD total score. Moreover, the feature selection step of this machinelearning procedure returned a subset of features that were predictiveand highly interpretable. Of the rs-fMRI connectivity features, theDefault Mode Network was the primary source, while other predictivefeatures were widely distributed across various resting-state networksincluding the Fronto-parietal Task Control, Salience,Somatosensory/motor, Subcortical, Attention, and Sensory networks.Structural features did not contribute much to the predictive strengthof this model, representing only about 1% of features found to bepredictive.

Altogether, the disclosed model provides an algorithm to predictdepression across multiple neurobehavioral disorders. The featuresimportant to this algorithm suggest that functional connectivity, ratherthan anatomy, provides a “depressive brain signature,” which could betargeted for intervention.

Additional Experimental Data Regarding the Disclosed Models

Table 7, below, shows metrics of the median mean square error (MSE), r²or variance explained, and p or features with non-zero regressioncoefficients for the three different model algorithms using the inputfeature set of individual scale items.

TABLE 7 Models with Scales as Input Feature Set Scales Only InputFeatures Model Algorithm Outcome Variables Metric Lasso ElasticNetRandomForest Mood/Dep_Hopkins median MSE 0.120 0.148 0.160 median r²0.550 0.530 0.510 p 30 50 n/a Mood_Bipolar median MSE 1.486 1.183 1.685median r² 0.791 0.836 0.745 p 53 112 n/a Anhedonia_Chapphy median MSE22.145 19.670 30.152 median r² 0.627 0.642 0.469 p 30 240 n/aAnhedonia_Chapsoc median MSE 11.127 12.510 20.134 median r² 0.796 0.7820.651 P 60 126 n/a Anxiety_Hopkins median MSE 0.123 0.134 0.183 medianr² 0.561 0.525 0.471 P 16 54 n/a median MSE 1.144 1.252 1.569Anxiety_Bipolar median r² 0.608 0.616 0.523 P 55 62 n/a

Table 8, below, shows metrics of median square error (MSE), r² orvariance explained, and p or features with non-zero regressioncoefficients for the three different model algorithms using the inputfeature set of sMRI measures (subcortical volume, cortical volume,etc.).

TABLE 8 Models with sMRI as Input Feature Set. sMRI Only Input FeaturesModel Algorithm Outcome Variables Metric Lasso ElasticNet RandomForestMood/Dep_Hopkins median MSE 0.282 0.299 0.283 median r² 0.026 0.0190.073 P 4 3 n/a Mood_Bipolar median MSE 6.604 6.408 5.957 median r²0.041 0.123 0.135 P 15 22 n/a Anhedonia_Chapphy median MSE 45.608 42.83551.740 median r² 0.083 0.158 0.019 P 29 61 n/a Anhedonia_Chapsoc medianMSE 46.644 43.893 48.993 median r² 0.131 0.065 0.072 P 32 32 n/aAnxiety_Hopkins median MSE 0.262 0.260 0.306 median r² 0.035 0.042 0.042P 3 6 n/a Anxiety_Bipolar median MSE 2.966 2.865 3.166 median r² 0.0550.121 0.034 P 15 16 n/a

Table 9, below, shows metrics of median square error (MSE), r² orvariance explained, and p or features with non-zero regressioncoefficients for the three different model algorithms using the inputfeature set of fMRI connectivity features.

TABLE 9 Models with fMRI as Input Feature Set. Predicted Scores MetricLasso ElasticNet RandomForest Chapman Social Anhedonia median MSE24.1366711 13.2464801 35.74676667 median r² 0.5908661 0.73192290.258061818 p 75 345 n/a Chapman Physical Anhedonia median MSE 30.90132921.4057029 39.74679883 median r² 0.61525222 0.65566807 0.247477881 p 76358 n/a HAMD, total score median MSE 33.2005788 38.9725415 71.34669412median r² 0.66777371 0.59569346 0.207388274 p 16 500 n/a HAMD, q1, 7, 8sum score median MSE 0.95578396 0.91593487 2.650023529 median r²0.79179955 0.74061902 0.332485876 p 28 191 n/a HAMD, q7 median MSE0.31201916 0.37790437 0.808070588 median r² 0.73200199 0.698810840.356340961 P 38 54 n/a BPRS, negative score median MSE 0.187579380.1506109 0.241262868 median r² 0.58051551 0.57369558 0.279867098 P 15131 n/a BPRS, depression-anxiety score median MSE 0.48898993 0.382283610.69174958 median r² 0.51218225 0.54291803 0.343333691 p 23 36 n/aHopkins, anxiety score median MSE 0.16332994 0.10991548 0.250750368median r² 0.4518252 0.65341477 0.307182751 p 24 85 n/a Hopkins,depression score median MSE 0.14661675 0.1591489 0.206618425 median r²0.48845674 0.45016098 0.280274072 p 31 29 n/a Bipolar II, depressionscore median MSE 2.2093303 2.53026198 4.058693333 median r² 0.643416780.62534857 0.323775594 p 50 255 n/a Bipolar II, anxiety score median MSE1.74502231 0.98772213 2.523746667 median r² 0.48106 0.64425450.304516018 p 43 153 n/a SANS, anhedonia factor score median MSE0.57249993 0.44668826 0.671989773 median r² 0.54341834 0.726031190.476297105 P 16 66 n/a SANS, avolition factor score median MSE0.38427335 0.41349887 0.523188636 median r² 0.60922858 0.681717140.393902033 P 20 63 n/a SANS, blunt affect factor score median MSE0.35097663 0.13853868 0.319771716 median r² 0.36340364 0.811128760.421756359 p 22 29 n/a SANS, alogia factor score median MSE 0.18042070.0918634 0.208887871 median r² 0.57666364 0.78317095 0.406578967 p 1537 n/a SANS, attention factor score median MSE 0.5114133 0.54501710.863740909 median r² 0.58044727 0.55803846 0.351176332 p 11 16 n/aSANS, anhedonia global score median MSE 0.6479366 0.653431 0.703936364median r² 0.57849286 0.5910022 0.567246117 p 13 22 n/a SANS, avolitionglobal score median MSE 0.65626457 0.45879379 1.112490909 median r²0.65501818 0.7356483 0.41697963 p 8 126 n/a SANS, blunt affect globalscore median MSE 0.76806032 0.40498193 0.725527273 median r² 0.50753240.67717999 0.438660494 p 20 18 n/a SANS, alogia global score median MSE0.18058636 0.25612571 0.360454545 median r² 0.71239653 0.449745540.20472973 p 12 21 n/a SANS, attention global score median MSE0.82370349 0.84473842 1.018854545 median r² 0.53408014 0.558623420.381446429 p 8 70 n/a

Table 10, below, shows metrics of median square error (MSE), r² orvariance explained, and p or features with non-zero regressioncoefficients for the three different model algorithms using the inputfeature set of sMRI and fMRI items.

TABLE 10 Models with sMRI + fMRI as Input Feature Set. Predicted ScoresMetric Lasso ElasticNet RandomForest Chapman Social Anhedonia median MSE20.3800964 14.682152 34.56825833 median r² 0.61287335 0.677293410.271666677 p 30 559 n/a Chapman Physical Anhedonia median MSE29.5944321 14.4191528 39.64058054 median r² 0.47730737 0.739883890.270972364 p 72 211 n/a HAMD, total score median MSE 40.44450227.0655892 65.45605995 median r² 0.46827069 0.61111933 0.317391988 p 13448 n/a HAMD, ql, 7, 8 sum score median MSE 2.36930173 1.30536192.42418007 median r² 0.57469992 0.78027275 0.415661799 p 21 78 n/a HAMD,q7 median MSE 0.48427727 0.44843705 0.733507692 median r² 0.517607480.62850068 0.374694444 p 4 95 n/a BPRS, negative score median MSE0.10894905 0.20808541 0.228798077 median r² 0.58309527 0.484166880.339701299 p 10 12 n/a BPRS, depression-anxiety score median MSE0.74672192 0.4527803 0.745567681 median r² 0.34811518 0.594212360.305609826 p 15 119 n/a Hopkins, anxiety score median MSE 0.120906940.09822079 0.189571301 median r² 0.55139531 0.65036656 0.303938076 p 829 n/a Hopkins, depression score median MSE 0.17540687 0.13838360.199493796 median r² 0.38018872 0.4151432 0.199243267 p 21 16 n/aBipolar II, depression score median MSE 3.58417568 1.867221824.104375843 median r² 0.45476109 0.71921058 0.297745091 p 48 241 n/aBipolar II, anxiety score median MSE 1.28026365 0.70325067 2.257191667median r² 0.60209066 0.78935698 0.32304625 p 25 161 n/a SANS, anhedoniafactor score median MSE 0.40512803 0.35700521 0.63381875 median r²0.71570239 0.77152059 0.552279155 p 8 48 n/a SANS, avolition factorscore median MSE 0.18738998 0.11302804 0.45203125 median r² 0.672810.84257768 0.519285076 p 7 41 n/a SANS, blunt affect factor score medianMSE 0.1055558 0.16598636 0.406562166 median r² 0.7535557 0.765321710.476367098 p 24 65 n/a SANS, alogia factor score median MSE 0.408590790.11637806 0.236031927 median r² 0.29255013 0.7725277 0.559877681 p 7 77n/a SANS, attention factor score median MSE 0.57872686 0.435846790.648390625 median r² 0.6007934 0.60740377 0.31332093 p 13 93 n/a SANS,anhedonia global score median MSE 0.88823187 0.57576394 1.095494073median r² 0.40161221 0.63872035 0.319304654 p 17 107 n/a SANS, avolitionglobal score median MSE 0.55331606 0.21081588 0.7125 median r²0.56105366 0.82726123 0.5096 p 6 67 n/a SANS, blunt affect global scoremedian MSE 0.76193885 0.26705401 0.75875 median r² 0.5411395 0.840124850.526436782 p 11 41 n/a SANS, alogia global score median MSE 0.528870250.25762776 0.3487375 median r² 0.31534639 0.65101623 0.320888889 p 8 26n/a SANS, attention global score median MSE 0.35172813 0.28886716 0.5875median r² 0.70380789 0.75674345 0.5975 p 8 13 n/a

Table 11, below, shows metrics of median square error (MSE), r² orvariance explained, and p or features with non-zero regressioncoefficients for the three different model algorithms using the inputfeature set of individual scale items and sMRI features.

TABLE 11 Models with Scales + sMRI as Input Feature Set. PredictedScores Metric Lasso ElasticNet RandomForest Chapman Social Anhedoniamedian MSE 10.1872913 10.8473571 23.01353415 median r² 0.81896870.79591289 0.599748178 p 59 63 n/a Chapman Physical Anhedonia median MSE15.1648738 15.1775051 31.00075366 median r² 0.6745091 0.690348220.429974564 p 92 123 n/a HAMD, total score median MSE 44.874316921.4111889 51.17416788 median r² 0.62386495 0.80822534 0.600713723 p 31123 n/a HAMD, ql, 7,8 sum score median MSE 2.04269051 1.257201562.447818182 median r² 0.59849127 0.76841313 0.474861019 p 38 110 n/aHAMD, q7 median MSE 0.5660152 0.37695234 0.771495455 median r²0.60961318 0.73435387 0.407349419 p 28 58 n/a BPRS, negative scoremedian MSE 0.22947823 0.11052677 0.223487784 median r² 0.35679010.68800207 0.426280069 p 12 54 n/a BPRS, depression-anxiety score medianMSE 0.47774909 0.37200915 0.716768024 median r² 0.6784626 0.767587060.520999457 p 39 58 n/a Hopkins, anxiety score median MSE 0.152784690.14505891 0.147720373 median r² 0.46629576 0.47103414 0.475010058 p 1449 n/a Hopkins, depression score median MSE 0.13015768 0.105608350.148762648 median r² 0.58405287 0.67747487 0.516387069 p 15 102 n/aBipolar II, depression score median MSE 1.11473489 1.020577291.722802026 median r² 0.84118256 0.86421896 0.745336441 p 32 114 n/aBipolar II, anxiety score median MSE 0.81608355 0.79832926 1.565412195median r² 0.7409768 0.73479011 0.523792469 p 30 58 n/a SANS, anhedoniafactor score median MSE 0.80659301 0.8876104 1.02542 median r² 0.48470420.43714204 0.251376657 p 24 13 n/a SANS, avolition factor score medianMSE 0.35767455 0.29545236 0.76713875 median r² 0.64133778 0.693488710.252067852 p 30 54 n/a SANS, blunt affect factor score median MSE0.29046712 0.35398284 0.555033335 median r² 0.65317772 0.576501950.346361453 p 15 29 n/a SANS, alogia factor score median MSE 0.273553050.22486147 0.370562213 median r² 0.43984006 0.45944781 0.260900092 p 1622 n/a SANS, attention factor score median MSE 0.61391724 0.529195150.840366667 median r² 0.42634181 0.56616671 0.26694625 p 15 90 n/a SANS,anhedonia global score median MSE 0.82696728 0.91638191 1.31 median r²0.52922904 0.52124193 0.349258197 p 16 57 n/a SANS, avolition globalscore median MSE 1.0735851 0.96201314 1.69874 median r² 0.489440930.4703767 0.186773404 p 15 92 n/a SANS, blunt affect global score medianMSE 0.47919468 0.64615998 0.941919557 median r² 0.56575973 0.59707390.403780513 p 16 89 n/a SANS, alogia global score median MSE 0.423197020.51674167 0.709333333 median r² 0.41947137 0.43807514 0.181628238 p 1516 n/a SANS, attention global score median MSE 0.5766073 0.879847861.165269856 median r² 0.53935525 0.37445084 0.141182351 p 23 56 n/a

Table 12, below, shows metrics of median square error (MSE), r² orvariance explained, and p or features with non-zero regressioncoefficients for the three different model algorithms using the inputfeature set of individual scale items and fMRI features.

TABLE 12 Models with Scales + fMRI as Input Feature Set. Scales + fMRIInput Features Model Algorithm Outcome Variables Metric Lasso ElasticNetRandomForest Mood/Dep_Hopkins median MSE 0.077 0.110 0.205 median r²0.709 0.610 0.360 P 16 26 n/a Mood_Bipolar median MSE 0.861 0.814 3.003median r² 0.869 0.874 0.600 P 50 236 n/a Anhedonia_Chapphy median MSE11.439 9.648 35.942 median r² 0.805 0.841 0.349 P 46 211 n/aAnhedonia_Chapsoc median MSE 7.549 8.627 23.482 median r² 0.857 0.8290.486 p 47 31 n/a Anxiety_Hopkins median MSE 0.150 0.095 0.205 median r²0.597 0.704 0.312 p 27 127 n/a Anxiety_Bipolar median MSE 0.928 0.5891.782 median r² 0.729 0.825 0.469 p 31 32 n/a

Table 13, below, shows metrics of median square error (MSE), r² orvariance explained, and p or features with non-zero regressioncoefficients for the three different model algorithms using the inputfeature set of individual scale items, sMRI, and fMRI features.

TABLE 13 Models with Scales + sMRI + fMRI as Input Feature Set. Scales +sMRI + fMRI Input Features Model Algorithm Outcome Variables MetricLasso ElasticNet RandomForest Mood/Dep_Hopkins median MSE 0.132 0.0760.129 median r² 0.514 0.721 0.470 p 8 28 n/a Mood_Bipolar median MSE0.724 0.614 2.163 median r² 0.874 0.904 0.658 p 31 93 n/aAnhedonia_Chapphy median MSE 23.104 15.814 27.952 median r² 0.620 0.6520.259 p 48 32 n/a Anhedonia_Chapsoc median MSE 7.027 9.886 25.091 medianr² 0.822 0.804 0.438 P 30 106 n/a Anxiety_Hopkins median MSE 0.086 0.0770.149 median r² 0.684 0.751 0.420 P 27 47 n/a Anxiety_Bipolar median MSE0.521 0.535 1.745 median r² 0.838 0.847 0.435 P 72 31 n/a

Table 14, below, shows Wilcoxon rank-sum U statistics and p-values for apost hoc group-comparison statistic of significantly different motionmeasures shown in FIGS. 7A-7I. Significant p-values can be identifiedfrom the table.

TABLE 14 Post-hoc group comparison statistic of significantly differentmotion measures shown in FIGS. 7A-7I. Motion Measures Group ComparisonMean FD Sharp Motion Y-axis Motion Z-axis Motion HC v. SZ U stat = 162 Ustat = 151 U stat = 167 U stat = 174 p = 0.0005 p = 0.0003 p = 0.0007 p= 0.0010 HC v. BD U stat = 458 U stat = 435 U stat = 438 U stat = 434 p= 0.0112 p = 0.0058 p = 0.0063 p = 0.0056 HC v. ADHD U stat = 581 U stat= 600 U stat = 628 U stat = 481 p = 0.2358 p = 0.3035 p = 0.4164 p =0.0357 SZ v. BD U stat = 132 U stat = 139 U stat = 140 U stat = 140 p =0.1067 p = 0.1493 p = 0.1562 p = 0.1562 SZ v. ADHD U stat = 91 U stat =90 U stat = 77 U stat = 112 p = 0.0103 p = 0.0095 p = 0.0031 p = 0.0465BD v. ADHD U stat = 232 U stat = 219 U stat = 205 U stat = 251 p =0.0885 p = 0.0537 p = 0.0294 p = 0.1660

Additional Embodiments

Additional aspects of the present disclosure include the followingmethod: Clinical scale data, resting-state functional-MRI data, andstructural-MRI scans are received for multiple patients withschizophrenia, bipolar disorder, attention deficit and hyperactivitydisorder (“ADHD”), or healthy controls. The received data arepreprocessed. At least one predictive model of symptom expression isgenerated based on the preprocessed data. Subsets of features in thereceived data are identified from the at least one predictive model topredict transdiagnostic symptoms related to depression, anxiety,anhedonia, and other negative symptoms.

Further aspects of the present disclosure include the following computersystem: A computing system includes at least one database, a memory, anda processor. The database stores clinical scale data, resting-statefunctional-MRI data, and structural-MRI scans for multiple patients withschizophrenia, bipolar disorder, ADHD, or healthy controls. The memorystores computer instructions. The processor that is configured toexecute the computer instructions to preprocess the data stored in theat least one database. At least one predictive model of symptomexpression is generated based on the preprocessed data. Subsets offeatures in the received data are identified from the at least onepredictive model to predict transdiagnostic symptoms related todepression, anxiety, anhedonia, and other negative symptoms.

Although the present disclosure provides for models trained on the CNPdatabase, the present disclosure contemplates that any databasecomprising clinical scales data and MRI data can be used to producemodels, as would be readily contemplated by one skilled in the art.

The disclosed models selected as informative the features which trend inthe same direction for all participants. The present disclosurecontemplates that brain activity can be examined which diverges betweenpatient groups; such an approach can yield other features.

Although the present disclosure discusses input primarily in terms offMRI data and sMRI data, other embodiments can provide for receivingrs-fMRI.

Altogether, the present disclosure provides a data-driven way to improvebiomarker development for predicting symptom severitytransdiagnostically and can be used in a personalized medicine approachin diagnosing and treating behavioral disorders.

Machine Learning Implementation

Various aspects of the present disclosure can be performed by amachine-learning algorithm, as readily understood by a person skilled inthe art. In some examples, step 1540 of FIG. 15 and methodology 1600 ofFIG. 16 can be performed by a supervised or unsupervised algorithm. Forinstance, the system may utilize more basic machine learning toolsincluding 1) decision trees (“DT”), (2) Bayesian networks (“BN”), (3)artificial neural network (“ANN”), or (4) support vector machines(“SVM”). In other examples, deep learning algorithms or other moresophisticated machine learning algorithms, e.g., convolutional neuralnetworks (“CNN”), or capsule networks (“CapsNet”) may be used.

DT are classification graphs that match input data to questions asked ateach consecutive step in a decision tree. The DT program moves down the“branches” of the tree based on the answers to the questions (e.g.,First branch: Did the clinical scales data include certain input? yes orno. Branch two: Did the MRI data include certain features? yes or no,etc.).

Bayesian networks (“BN”) are based on likelihood something is true basedon given independent variables and are modeled based on probabilisticrelationships. BN are based purely on probabilistic relationships thatdetermine the likelihood of one variable based on another or others. Forexample, BN can model the relationships between MRI data, clinicalscales data, and any other information as contemplated by the presentdisclosure. Particularly, if a question type and particular features ofthe patient's MRI data are known, a BN can be used to compute a symptomseverity indicator. Thus, using an efficient BN algorithm, an inferencecan be made based on the input data.

Artificial neural networks (“ANN”) are computational models inspired byan animal's central nervous system. They map inputs to outputs through anetwork of nodes. However, unlike BN, in ANN the nodes do notnecessarily represent any actual variable. Accordingly, ANN may have ahidden layer of nodes that are not represented by a known variable to anobserver. ANNs are capable of pattern recognition. Their computingmethods make it easier to understand a complex and unclear process thatmight go on during determining a symptom severity indicator based on avariety of input data.

Support vector machines (“SVM”) came about from a framework utilizing ofmachine learning statistics and vector spaces (linear algebra conceptthat signifies the number of dimensions in linear space) equipped withsome kind of limit-related structure. In some cases, they may determinea new coordinate system that easily separates inputs into twoclassifications. For example, a SVM could identify a line that separatestwo sets of points originating from different classifications of events.

Deep neural networks (DNN) have developed recently and are capable ofmodeling very complex relationships that have a lot of variation.Various architectures of DNN have been proposed to tackle the problemsassociated with algorithms such as ANN by many researchers during thelast few decades. These types of DNN are CNN (Convolutional NeuralNetwork), RBM (Restricted Boltzmann Machine), LSTM (Long Short TermMemory) etc. They are all based on the theory of ANN. They demonstrate abetter performance by overcoming the back-propagation error diminishingproblem associated with ANN.

Machine learning models require training data to identify the featuresof interest that they are designed to detect. For instance, variousmethods may be utilized to form the machine learning models, includingapplying randomly assigned initial weights for the network and applyinggradient descent using back propagation for deep learning algorithms. Inother examples, a neural network with one or two hidden layers can beused without training using this technique.

In some examples, the machine learning model can be trained usinglabeled data, or data that represents certain user input. In otherexamples, the data will only be labeled with the outcome and the variousrelevant data may be input to train the machine learning algorithm.

For instance, to determine whether particular mental health disorderfits the input data, various machine learning models may be utilizedthat input various data disclosed herein. In some examples, the inputdata will be labeled by having an expert in the field label the relevantregulations according to the particular situation. Accordingly, theinput to the machine learning algorithm for training data identifiesvarious data as from a healthy control or from a patient.

Exemplary NMR System

Referring now to FIGS. 17A-18, the methods and embodiments of thepresent disclosure can be performed on an exemplary nuclear magneticresonance (“NMR system”). As a person of ordinary skill in the artunderstands, NMR commonly refers to the hardware used to generatedifferent types of scans, including MRI scans. Referring now to FIGS.17A-18, there is shown the major components of an NMR system which canbe used to carry out the methods of the various embodiments. FIG. 18shows the components of an exemplary transceiver for the NMR system ofFIGS. 17A-17B. It should be noted that the methods of the variousembodiments can also be carried out using other NMR systems.

The operation of the system of FIGS. 17A-18 is controlled from anoperator console 100 which includes a console processor 101 that scans akeyboard 102 and receives inputs from a human operator through a controlpanel 103 and a plasma display/touch screen 104. The console processor101 communicates through a communications link 116 with an applicationsinterface module 117 in a separate computer system 107. Through thekeyboard 102 and controls 103, an operator controls the production anddisplay of images by an image processor 106 in the computer system 107,which connects directly to a video display 118 on the console 100through a video cable 105.

The computer system 107 is formed about a backplane bus which conformswith the VME standards, and it includes a number of modules whichcommunicate with each other through this backplane. In addition to theapplication interface 117 and the image processor 106, these include aCPU module 108 that controls the VME backplane, and an SCSI interfacemodule 109 that connects the computer system 107 through a bus 110 to aset of peripheral devices, including disk storage 111 and tape drive112. The computer system 107 also includes a memory module 113, known inthe art as a frame buffer for storing image data arrays, and a serialinterface module 114 that links the computer system 107 through a highspeed serial link 115 to a system interface module 120 located in aseparate system control cabinet 122.

The system control 122 includes a series of modules which are connectedtogether by a common backplane 118. The backplane 118 is comprised of anumber of bus structures, including a bus structure which is controlledby a CPU module 119. The serial interface module 120 connects thisbackplane 118 to the high speed serial link 115, and pulse generatormodule 121 connects the backplane 118 to the operator console 100through a serial link 125. It is through this link 125 that the systemcontrol 122 receives commands from the operator which indicate the scansequence that is to be performed.

The pulse generator module 121 operates the system components to carryout the desired scan sequence. It produces data which indicates thetiming, strength and shape of the RF pulses which are to be produced,and the timing of and length of the data acquisition window. The pulsegenerator module 121 also connects through serial link 126 to a set ofgradient amplifiers 127, and it conveys data thereto which indicates thetiming and shape of the gradient pulses that are to be produced duringthe scan. The pulse generator module 121 also receives patient datathrough a serial link 128 from a physiological acquisition controller129. The physiological acquisition control 129 can receive a signal froma number of different sensors connected to the patient. For example, itmay receive ECG signals from electrodes or respiratory signals from abellows and produce pulses for the pulse generator module 121 thatsynchronizes the scan with the patient's cardiac cycle or respiratorycycle. And finally, the pulse generator module 121 connects through aserial link 132 to scan room interface circuit 133 which receivessignals at inputs 135 from various sensors associated with the positionand condition of the patient and the magnet system. It is also throughthe scan room interface circuit 133 that a patient positioning system134 receives commands which move the patient cradle and transport thepatient to the desired position for the scan.

The gradient waveforms produced by the pulse generator module 121 areapplied to a gradient amplifier system 127 comprised of Gx, Gy, and Gzamplifiers 136, 137 and 138, respectively. Each amplifier 136, 137, and138 is utilized to excite a corresponding gradient coil in an assemblygenerally designated 139. The gradient coil assembly 139 forms part of amagnet assembly 155 which includes a polarizing magnet 140 that producesa 1.5 Tesla polarizing field that extends horizontally through a bore.The gradient coils 139 encircle the bore, and when energized, theygenerate magnetic fields in the same direction as the main polarizingmagnetic field, but with gradients Gx, Gy and Gz directed in theorthogonal x-, y- and z-axis directions of a Cartesian coordinatesystem. That is, if the magnetic field generated by the main magnet 140is directed in the z direction and is termed BO, and the total magneticfield in the z direction is referred to as Bz, then Gx∂Bz/∂x, Gy=∂Bz/∂yand Gz=∂Bz/∂z, and the magnetic field at any point (x,y,z) in the boreof the magnet assembly 141 is given by B(x,y,z)=Bo+Gxx+GyyGzz. Thegradient magnetic fields are utilized to encode spatial information intothe NMR signals emanating from the patient being scanned. Because thegradient fields are switched at a very high speed when an EPI sequenceis used to practice the preferred embodiment of the invention, localgradient coils are employed in place of the whole-body gradient coils139. These local gradient coils are designed for the head and are inclose proximity thereto. This enables the inductance of the localgradient coils to be reduced and the gradient switching rates increasedas required for the EPI pulse sequence. For a description of these localgradient coils which is incorporated herein by reference, see U.S. Pat.No. 5,372,137 issued on Dec. 13, 1994, and entitled “NMR Local Coil ForBrain Imaging”.

Located within the bore 142 is a circular cylindrical whole-body RF coil152. This coil 152 produces a circularly polarized RF field in responseto RF pulses provided by a transceiver module 150 in the system controlcabinet 122. These pulses are amplified by an RF amplifier 151 andcoupled to the RF coil 152 by a transmit/receive switch 154 which formsan integral part of the RF coil assembly. Waveforms and control signalsare provided by the pulse generator module 121 and utilized by thetransceiver module 150 for RF carrier modulation and mode control. Theresulting NMR signals radiated by the excited nuclei in the patient maybe sensed by the same RF coil 152 and coupled through thetransmit/receive switch 154 to a preamplifier 153. The amplified NMRsignals are demodulated, filtered, and digitized in the receiver sectionof the transceiver 150.

The transmit/receive switch 154 is controlled by a signal from the pulsegenerator module 121 to electrically connect the RF amplifier 151 to thecoil 152 during the transmit mode and to connect the preamplifier 153during the receive mode. The transmit/receive switch 154 also enables aseparate local RF head coil to be used in the transmit and receive modeto improve the signal-to-noise ratio of the received NMR signals. Withcurrently available NMR systems such a local RF coil is preferred inorder to detect small variations in NMR signal. Reference is made to theabove cited U.S. Pat. No. 5,372,137 for a description of the preferredlocal RF coil.

In addition to supporting the polarizing magnet 140 and the gradientcoils 139 and RF coil 152, the main magnet assembly 141 also supports aset of shim coils 156 associated with the main magnet 140 and used tocorrect inhomogeneities in the polarizing magnet field. The main powersupply 157 is utilized to bring the polarizing field produced by thesuperconductive main magnet 140 to the proper operating strength and isthen removed.

The NMR signals picked up by the RF coil are digitized by thetransceiver module 150 and transferred to a memory module 160 which isalso part of the system control 122. When the scan is completed and anentire array of data has been acquired in the memory modules 160, anarray processor 161 operates to Fourier transform the data into an arrayof image data. This image data is conveyed through the serial link 115to the computer system 107 where it is stored in the disk memory 111. Inresponse to commands received from the operator console 100, this imagedata may be archived on the tape drive 112, or it may be furtherprocessed by the image processor 106 and conveyed to the operatorconsole 100 and presented on the video display 118 as will be describedin more detail hereinafter.

Referring particularly to FIG. 18, the transceiver 150 includescomponents which produce the RF excitation field B1 through poweramplifier 151 at a coil 152A and components which receive the resultingNMR signal induced in a coil 152B. As indicated above, the coils 152Aand B may be a single whole-body coil, but the best results are achievedwith a single local RF coil specially designed for the head. The base orcarrier frequency of the RF excitation field is produced under controlof a frequency synthesizer 200 which receives a set of digital signals(CF) through the backplane 118 from the CPU module 119 and pulsegenerator module 121. These digital signals indicate the frequency andphase of the RF carrier signal, which is produced at an output 201. Thecommanded RF carrier is applied to a modulator and up converter 202where its amplitude is modulated in response to a signal R(t) alsoreceived through the backplane 118 from the pulse generator module 121.The signal R(t) defines the envelope, and therefore the bandwidth, ofthe RF excitation pulse to be produced. It is produced in the module 121by sequentially reading out a series of stored digital values thatrepresent the; desired envelope. These stored digital values may, inturn, be changed from the operator console 100 to enable any desired RFpulse envelope to be produced. The modulator and up converter 202produces an RF pulse at the desired Larmor frequency at an output 205.The magnitude of the RF excitation pulse output through line 205 isattenuated by an exciter attenuator circuit 206 which receives a digitalcommand, TA, from the backplane 118. The attenuated RF excitation pulsesare applied to the power amplifier 151 that drives the RF coil 152A. Fora more detailed description of this portion of the transceiver 122,reference is made to U.S. Pat. No. 4,952,877, which is incorporatedherein by reference.

Referring still to FIGS. 17A-18, the NMR signal produced by the subjectis picked up by the receiver coil 152B and applied through thepreamplifier 153 to the input of a receiver attenuator 207. The receiverattenuator 207 further amplifies the NMR signal, and this is attenuatedby an amount determined by a digital attenuation signal (RA) receivedfrom the backplane 118. The receive attenuator 207 is also turned on andoff by a signal from the pulse generator module 121 such that it is notoverloaded during RF excitation. The received NMR signal is at or aroundthe Larmor frequency, which in the preferred embodiment is around 63.86MHz for 1.5 Tesla. This high-frequency signal is down-converted in atwo-step process by a down converter 208 which first mixes the NMRsignal with the carrier signal on line 201 and then mixes the resultingdifference signal with the 2.5 MHz reference signal on line 204. Theresulting down-converted NMR signal on line 212 has a maximum bandwidthof 125 kHz, and it is centered at a frequency of 187.5 kHz. Thedown-converted NMR signal is applied to the input of ananalog-to-digital (A/D) converter 209, which samples and digitizes theanalog signal at a rate of 250 kHz. The output of the A/D converter 209is applied to a digital detector, and signal processor 210 which produce16-bit in-phase (I) values and 16-bit quadrature (Q) valuescorresponding to the received digital signal. The resulting stream ofdigitized I and Q values of the received NMR signal is output throughbackplane 118 to the memory module 160 where they are employed toreconstruct an image.

To preserve the phase information contained in the received NMR signal,both the modulator and up converter 202 in the exciter section and thedown converter 208 in the receiver section are operated with commonsignals. More particularly, the carrier signal at the output 201 of thefrequency synthesizer 200 and the 2.5 MHz reference signal at the output204 of the reference frequency generator 203 are employed in bothfrequency conversion processes. Phase consistency is thus maintained,and phase changes in the detected NMR signal accurately indicate phasechanges produced by the excited spins. The 2.5 MHz reference signal aswell as 5, 10 and 60 MHz reference signals are produced by the referencefrequency generator 203 from a common 20 MHz master clock signal. Thelatter three reference signals are employed by the frequency synthesizer200 to produce the carrier signal on output 201. For a more detaileddescription of the receiver, reference is made to U.S. Pat. No.4,992,736, which is incorporated herein by reference.

Computer & Hardware Implementation of Disclosure

It should initially be understood that the disclosure herein may beimplemented with any type of hardware and/or software, and may be apre-programmed general purpose computing device. For example, the systemmay be implemented using a server, a personal computer, a portablecomputer, a thin client, or any suitable device or devices. Thedisclosure and/or components thereof may be a single device at a singlelocation, or multiple devices at a single, or multiple, locations thatare connected together using any appropriate communication protocolsover any communication medium such as electric cable, fiber optic cable,or in a wireless manner.

It should also be noted that the disclosure is illustrated and discussedherein as having a plurality of modules which perform particularfunctions. It should be understood that these modules are merelyschematically illustrated based on their function for clarity purposesonly, and do not necessary represent specific hardware or software. Inthis regard, these modules may be hardware and/or software implementedto substantially perform the particular functions discussed. Moreover,the modules may be combined together within the disclosure, or dividedinto additional modules based on the particular function desired. Thus,the disclosure should not be construed to limit the present invention,but merely be understood to illustrate one example implementationthereof.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someimplementations, a server transmits data (e.g., an HTML page) to aclient device (e.g., for purposes of displaying data to and receivinguser input from a user interacting with the client device). Datagenerated at the client device (e.g., a result of the user interaction)can be received from the client device at the server.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back-endcomponent (e.g., as a data server) or a middleware component (e.g., anapplication server) or a front-end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the subject matter described inthis specification) or any combination of one or more such back-end,middleware, or front-end components. The components of the system can beinterconnected by any form or medium of digital data communication(e.g., a communication network). Examples of communication networksinclude a local area network (“LAN”) and a wide area network (“WAN”), aninter-network (e.g., the Internet), and peer-to-peer networks (e.g., adhoc peer-to-peer networks).

Implementations of the subject matter and the operations described inthis specification can be implemented in digital electronic circuitry,or in computer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Implementations of the subjectmatter described in this specification can be implemented as one or morecomputer programs (i.e., one or more modules of computer programinstructions) encoded on computer storage medium for execution by, or tocontrol the operation of, data processing apparatus. Alternatively or inaddition, the program instructions can be encoded on anartificially-generated propagated signal (e.g., a machine-generatedelectrical, optical, or electromagnetic signal) that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. A computer storage medium canbe, or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially-generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate physical components or media (e.g., multiple CDs, disks, orother storage devices).

The operations described in this specification can be implemented asoperations performed by a “data processing apparatus” on data stored onone or more computer-readable storage devices or received from othersources.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing The apparatus can includespecial purpose logic circuitry (e.g., an FPGA (field-programmable gatearray) or an ASIC (application-specific integrated circuit)). Theapparatus can also include, in addition to hardware, code that createsan execution environment for the computer program in question (e.g.,code that constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more ofthem). The apparatus and execution environment can realize variousdifferent computing model infrastructures, such as web services,distributed computing, and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry (e.g., an FPGA (field-programmable gate array) or an ASIC(application-specific integrated circuit)).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for performing actions in accordance with instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data (e.g., magnetic, magneto-optical disks, or optical disks).However, a computer need not have such devices. Moreover, a computer canbe embedded in another device (e.g., a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), to name just a few).Devices suitable for storing computer program instructions and datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices (e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks). The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

CONCLUSION

The various methods and techniques described above provide a number ofways to carry out the invention. Of course, it is to be understood thatnot necessarily all objectives or advantages described can be achievedin accordance with any particular embodiment described herein. Thus, forexample, those skilled in the art will recognize that the methods can beperformed in a manner that achieves or optimizes one advantage or groupof advantages as taught herein without necessarily achieving otherobjectives or advantages as taught or suggested herein. A variety ofalternatives are mentioned herein. It is to be understood that someembodiments specifically include one, another, or several features,while others specifically exclude one, another, or several features,while still others mitigate a particular feature by inclusion of one,another, or several advantageous features.

Furthermore, the skilled artisan will recognize the applicability ofvarious features from different embodiments. Similarly, the variouselements, features, and steps discussed above, as well as other knownequivalents for each such element, feature or step, can be employed invarious combinations by one of ordinary skill in this art to performmethods in accordance with the principles described herein. Among thevarious elements, features, and steps, some will be specificallyincluded and others specifically excluded in diverse embodiments.

Although the application has been disclosed in the context of certainembodiments and examples, it will be understood by those skilled in theart that the embodiments of the application extend beyond thespecifically disclosed embodiments to other alternative embodimentsand/or uses and modifications and equivalents thereof.

In some embodiments, the terms “a” and “an” and “the” and similarreferences used in the context of describing a particular embodiment ofthe application (especially in the context of certain of the followingclaims) can be construed to cover both the singular and the plural. Therecitation of ranges of values herein is merely intended to serve as ashorthand method of referring individually to each separate valuefalling within the range. Unless otherwise indicated herein, eachindividual value is incorporated into the specification as if it wereindividually recited herein. All methods described herein can beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context. The use of any and allexamples, or exemplary language (for example, “such as”) provided withrespect to certain embodiments herein is intended merely to betterilluminate the application and does not pose a limitation on the scopeof the application otherwise claimed. No language in the specificationshould be construed as indicating any non-claimed element essential tothe practice of the application.

Certain embodiments of this application are described herein. Variationson those embodiments will become apparent to those of ordinary skill inthe art upon reading the foregoing description. It is contemplated thatskilled artisans can employ such variations as appropriate, and theapplication can be practiced otherwise than specifically describedherein. Accordingly, many embodiments of this application include allmodifications and equivalents of the subject matter recited in theclaims appended hereto as permitted by applicable law. Moreover, anycombination of the above-described elements in all possible variationsthereof is encompassed by the application unless otherwise indicatedherein or otherwise clearly contradicted by context.

Particular implementations of the subject matter have been described.Other implementations are within the scope of the following claims. Insome cases, the actions recited in the claims can be performed in adifferent order and still achieve desirable results. In addition, theprocesses depicted in the accompanying figures do not necessarilyrequire the particular order shown, or sequential order, to achievedesirable results.

All patents, patent applications, publications of patent applications,and other material, such as articles, books, specifications,publications, documents, things, and/or the like, referenced herein arehereby incorporated herein by this reference in their entirety for allpurposes, excepting any prosecution file history associated with same,any of same that is inconsistent with or in conflict with the presentdocument, or any of same that may have a limiting affect as to thebroadest scope of the claims now or later associated with the presentdocument. By way of example, should there be any inconsistency orconflict between the description, definition, and/or the use of a termassociated with any of the incorporated material and that associatedwith the present document, the description, definition, and/or the useof the term in the present document shall prevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that can be employedcan be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication can be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

While various examples of the present disclosure have been describedabove, it should be understood that they have been presented by way ofexample only, and not limitation. Numerous changes to the disclosedexamples can be made in accordance with the disclosure herein withoutdeparting from the spirit or scope of the disclosure. Thus, the breadthand scope of the present disclosure should not be limited by any of theabove described examples. Rather, the scope of the disclosure should bedefined in accordance with the following claims and their equivalents.

Although the disclosure has been illustrated and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art upon the reading andunderstanding of this specification and the annexed drawings. Inaddition, while a particular feature of the disclosure may have beendisclosed with respect to only one of several implementations, suchfeature may be combined with one or more other features of the otherimplementations as may be desired and advantageous for any given orparticular application.

The terminology used herein is for the purpose of describing particularexamples only and is not intended to be limiting of the disclosure. Asused herein, the singular forms “a,” “an,” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. Furthermore, to the extent that the terms “including,”“includes,” “having,” “has,” “with,” or variants thereof, are used ineither the detailed description and/or the claims, such terms areintended to be inclusive in a manner similar to the term “comprising.”

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure belongs.Furthermore, terms, such as those defined in commonly used dictionaries,should be interpreted as having a meaning that is consistent with theirmeaning in the context of the relevant art, and will not be interpretedin an idealized or overly formal sense unless expressly so definedherein.

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What is claimed is:
 1. A system for evaluating mental health of apatient, the system comprising: a display device; a user interface; amemory containing machine readable medium comprising machine executablecode having stored thereon instructions for performing a method; acontrol system coupled to the memory comprising one or more processors,the control system configured to execute the machine executable code tocause the control system to: receive, from the user interface, aselection of answers corresponding to each question in a series ofquestions from mental health questionnaires; receive, unprocessed MRIdata corresponding to a set of MRI images of a biological structure;process, using a machine learning model, the selection of answers, andthe unprocessed MRI data to output a symptom severity indicator for amental health category of the patient, wherein the machine learningmodel was generated by: receiving labeled training data for a pluralityof individuals indicating whether each of the plurality of individualshas one or more mental health disorders and a severity of symptomscorresponding to the one or more mental health disorders, the labeledtraining data comprising: MRI data recorded for each of the plurality ofindividuals; a selection of answers to the series of questions for eachof the plurality of individuals; determining a plurality of featuresfrom the labeled training data; training an initial machine learningmodel in a supervised manner, based on the plurality of features;extracting importance measures for each of the plurality of features,based on the training of the initial machine learning model; generatinga plurality of subset machine learning models based on the extractedimportance measures for the plurality of features; evaluating aclassification performance of the generated plurality of subset machinelearning models; and selecting at least one of the subset machinelearning models as the machine learning model.
 2. The system of claim 1,wherein the machine learning model is trained on clinical scales datacorresponding to the plurality of individuals.
 3. The system of claim 1,wherein the machine learning model is trained on fMRI full connectivitydata corresponding to the plurality of individuals.
 4. The system ofclaim 1, wherein the machine learning model is trained on sMRI datacorresponding to the plurality of individuals, the sMRI data comprisingcortical volume data, cortical thickness data, and cortical surface areadata.
 5. The system of claim 1, wherein the machine learning model istrained on input data corresponding to the plurality of individuals,wherein, for each individual, the input data comprises clinical scalesdata and fMRI data.
 6. The system of claim 1, wherein the machinelearning model is trained on input data corresponding to the pluralityof individuals, wherein, for each individual, the input data comprisesclinical scales data and sMRI data.
 7. The system of claim 1, whereinthe machine learning model is trained on input data corresponding to theplurality of individuals, wherein, for each individual, the input datacomprises fMRI data and sMRI data.
 8. The system of claim 1, wherein themachine learning model is trained on input data corresponding to theplurality of individuals, wherein, for each individual, the input datacomprises fMRI data, clinical scales data, and sMRI data.